@article {868, title = {A Large-Scale Empirical Study of LLM Orchestration and Ensemble Strategies for Sentiment Analysis in Recommender Systems}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {112}, abstract = {

This paper presents a comprehensive empirical evaluation comparing meta-model aggregation strategies with traditional ensemble methods and standalone models for sentiment analysis in recommender systems beyond standalone large language model (LLM) performance. We investigate whether aggregating multiple LLMs through a reasoning-based meta-model provides measurable performance advantages over individual models and standard statistical aggregation approaches in zero-shot sentiment classification. Using a balanced dataset of 5000 verified Amazon purchase reviews (1000 reviews per rating category from 1 to 5 stars, sampled via two-stage stratified sampling across five product categories), we evaluate 12 different leading pre-trained LLMs from four major providers (OpenAI, Anthropic, Google, and DeepSeek) in both standalone and meta-model configurations. Our experimental design systematically compares individual model performance against GPT-based meta-model aggregation and traditional ensemble baselines (majority voting, mean aggregation). Results show statistically significant improvements (McNemar{\textquoteright}s test, p \< 0.001): the GPT-5 meta-model achieves 71.40\% accuracy (10.15 percentage point improvement over the 61.25\% individual model average), while the GPT-5 mini meta-model reaches 70.32\% (9.07 percentage point improvement). These observed improvements surpass traditional ensemble methods (majority voting: 62.64\%; mean aggregation: 62.96\%), suggesting potential value in meta-model aggregation for sentiment analysis tasks. Our analysis reveals empirical patterns including neutral sentiment classification challenges (3-star ratings show 64.83\% failure rates across models), model influence hierarchies, and cost-accuracy trade-offs ($130.45 aggregation cost vs. $0.24{\textendash}$43.97 for individual models per 5000 predictions). This work provides evidence-based insights into the comparative effectiveness of LLM aggregation strategies in recommender systems, demonstrating that meta-model aggregation with natural language reasoning capabilities achieves measurable performance gains beyond statistical aggregation alone.

}, keywords = {empirical evaluation, ensemble learning, large language models, LLM orchestration, meta-model aggregation, multi-model aggregation, reasoning models, Recommender Systems, sentiment classification, zero-shot sentiment analysis}, issn = {1999-5903}, doi = {https://doi.org/10.3390/fi18020112}, url = {https://www.mdpi.com/1999-5903/18/2/112}, author = {Roumeliotis, Konstantinos I. and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @article {860, title = {An evaluation review of user similarity metrics in sparse collaborative filtering datasets}, journal = {International Journal of Data Science and Analytics}, year = {2025}, abstract = {

Collaborative filtering (CF) is one of the most prominent recommender system (RecSys) techniques of the recent years. CF generates rating predictions for the items that the user has not evaluated yet, using the evaluations of users with similar likings to the same items. Therefore, in CF the task of finding these users (which can be considered as reliable recommenders) is of high importance, while this task is especially challenging on sparse datasets. To this end, many user similarity metrics have been introduced and used in the literature, such as the Vector (or Cosine) Similarity metric, the Spearman rank correlation, the Pearson Correlation Coefficient (PCC), and others. For a CF RecSys, the use of the most efficient similarity metric is of great importance. This paper assesses the effectiveness of 15 user similarity metrics in sparse CF datasets, by conducting an extensive set of experiments. These experiments include 10 sparse CF datasets with diverse item domains, two neighbour selection approaches, two rating prediction formulas, and three rating prediction accuracy metrics. The evaluation results show that the metrics that achieve the best prediction results are found to be the Spearman rank correlation, followed by the Adjusted Rand Index, the Constrained PCC, and the Chebysev distance. Interestingly, the most widely used similarity metrics in CF research, i.e. the PCC and the Cosine Similarity, are not among the best performing metrics.

}, keywords = {collaborative filtering, Evaluation review, Recommender Systems, Sparse Datasets, User similarity metrics}, issn = {2364-4168}, doi = {10.1007/s41060-025-00846-4}, author = {Sgardelis, Kiriakos and Margaris, Dionisis and Spiliotopoulos, Dimitris and Vassilakis, Costas} } @article {852, title = {From Rating Predictions to Reliable Recommendations in Collaborative Filtering: The Concept of Recommendation Reliability Classes}, journal = {Big Data and Cognitive Computing}, volume = {9}, year = {2025}, pages = {106}, abstract = {

Recommender systems aspire to provide users with recommendations that have a high probability to be accepted. This is accomplished by producing rating predictions for products that they have not evaluated and afterwards, the products having the highest prediction scores are recommended to them. Collaborative filtering is a popular recommender system technique which generates rating prediction scores by blending the ratings that users with similar likings have previously set to these products. However, predictions may entail errors, which will either lead to recommending products that the users would not accept or failing to recommend products that the users would really accept. The first case is considered much more critical, since the recommender system will significantly lose reliability and consequently interest. In this paper, after performing a study on rating prediction confidence factors in collaborative filtering (a) we introduce the concept of prediction reliability classes, (b) we rank these classes in relation to the utility of the rating predictions belonging to each class and (c) we present a collaborative filtering recommendation algorithm which exploits these reliability classes for prediction formulation. The efficacy of the presented algorithm is evaluated through an extensive multi-parameter evaluation process, which demonstrates that it significantly enhances recommendation quality.

}, keywords = {collaborative filtering, Evaluation, Rating Predictions, Recommender Systems, reliability classes, reliable recommendations}, issn = {2504-2289}, doi = {10.3390/bdcc9040106}, author = {Dionisis Margaris and Costas Vassilakis and Spiliotopoulos, Dimitris} } @article {854, title = {Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality}, journal = {Technologies}, volume = {13}, year = {2025}, pages = {181}, abstract = {

Recommender systems suggest items that users are likely to accept by predicting ratings for items they have not already rated. Collaborative filtering is a widely used method that produces these predictions, based on the ratings of similar users, termed as near neighbors. However, in many cases, prediction errors occur and, therefore, the recommender system ends up either recommending unwanted products or missing out on products the user would actually desire. As a result, the quality of the recommendations that are produced is of major importance. In this paper, we introduce an advanced collaborative filtering recommendation algorithm that upgrades the quality of the recommendations that are produced by considering, along with the rating prediction value of the items computed by the plain collaborative filtering procedure, a number of confidence factors that each rating prediction fulfills. The presented algorithm maintains high recommendation coverage, and can be applied to every collaborative filtering dataset, since it is based only on the very basic information. Based on the application of the algorithm on widely used recommender systems datasets, the proposed algorithm significantly upgrades the recommendation quality, surpassing the performance of state-of-the-art research works that also consider confidence factors.

}, keywords = {Algorithm, collaborative filtering, prediction confidence factors, recommendation quality, Recommender Systems}, issn = {2227-7080}, doi = {https://doi.org/10.3390/technologies13050181}, url = {https://doi.org/10.3390/technologies13050181}, author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Sgardelis, Kiriakos and Costas Vassilakis} } @article {841, title = {Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering}, journal = {Big Data and Cognitive Computing}, volume = {8}, year = {2024}, pages = {53}, abstract = {

Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.

}, keywords = {Algorithm, collaborative filtering, confidence, Evaluation, Rating Predictions, Recommender Systems, Uncertainty}, issn = {2504-2289}, doi = {10.3390/bdcc8060053}, author = {Dionisis Margaris and Sgardelis, Kiriakos and Spiliotopoulos, Dimitris and Costas Vassilakis} } @conference {839, title = {Improving Recommendation Quality in Collaborative Filtering by Including Prediction Confidence Factors}, booktitle = {Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 24)}, year = {2024}, publisher = {SCITEPRESS Digital Library}, organization = {SCITEPRESS Digital Library}, abstract = {

Collaborative filtering is a prevalent recommender system technique which generates rating predictions based on the rating values given by the users{\textquoteright} near neighbours. Consequently, for each user, the items scoring the highest prediction values are recommended to them. Unfortunately, predictions inherently entail errors, which, in the case of recommender systems, manifest as unsuccessful recommendations. However, along with each rating prediction value, prediction confidence factors can be computed. As a result, items having low prediction confidence factor values, can be either declined for recommendation or have their recommendation priority demoted. In the former case, some users may receive fewer recommended items or even none, especially when using a sparse dataset. In this paper, we present an algorithm that determines the items to be recommended by considering both the rating prediction values and confidence factors of predictions, allowing for predictions with higher confidence factors to outrank predictions with higher value, but lower confidence. The presented algorithm achieves to enhance the recommendation quality, while at the same time retaining the number of recommendations for each user.

}, keywords = {Algorithm, Certainty factors, collaborative filtering, Rating Predictions, Recommender Systems}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis and Stefanos Ougiaroglou} } @article {818, title = {Disruptive Technologies for Parliaments: A Literature Review}, journal = {Future Internet}, volume = {15}, year = {2023}, month = {feb}, pages = {66}, abstract = {

Exploitation and use of disruptive technologies, such as the Internet of Things, recommender systems, and artificial intelligence, with an ambidextrous balance, are a challenge, nowadays. Users of the technologies, and stakeholders, could be part of a new organisational model that affects business procedures and processes. Additionally, the use of inclusive participatory organisational models is essential for the effective adoption of these technologies. Such models aim to transform organisational structures, as well. Public organisations, such as the parliament, could utilise information systems{\textquoteright} personalisation techniques. As there are a lot of efforts to define the framework, the methodology, the techniques, the platforms, and the suitable models for digital technologies adoption in public organisations, this paper aims to provide a literature review for disruptive technology inclusive use in parliaments. The review emphasises the assessment of the applicability of the technologies, their maturity and usefulness, user acceptance, their performance, and their correlation to the adoption of relevant innovative, inclusive organisational models. It is argued that the efficient digital transformation of democratic institutions, such as parliaments, with the use of advanced e-governance tools and disruptive technologies, requires strategic approaches for adoption, acceptance, and inclusive service adaptation.

}, keywords = {artificial intelligence, digital transformation, inclusiveness, Internet of Things, parliament, Recommender Systems}, doi = {https://doi.org/10.3390/fi15020066}, url = {https://www.mdpi.com/1999-5903/15/2/66}, author = {Dimitris Koryzis and Dionisis Margaris and Costas Vassilakis and Konstantinos Kotis and Dimitris Spiliotopoulos} } @article {820, title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets}, journal = {Big Data and Cognitive Computing}, volume = {7}, year = {2023}, month = {mar}, pages = {59}, abstract = {

Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.

}, keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability}, doi = {10.3390/bdcc7020059}, url = {https://www.mdpi.com/2504-2289/7/2/59}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou} } @conference {830, title = {A User Interface for Tuning QoS parameters in Recommendation-Based Business Process Scenario Adaptation}, booktitle = {Proceedings of the 19th International Conference on Web Information Systems and Technologies (WEBIST 2023)}, year = {2023}, abstract = {

The Web Services Business Process Execution Language (BPEL) is a special-purpose language that orchestrates web services into a high-level business process. A typical BPEL scenario contains invocations to preselected web services, along with their parameters. However, many recent research works support dynamic service selection, based on user-set policies and criteria. Furthermore, users may request a service recommendation, in which case functionally equivalent service offerings by different providers will be considered by the personalization module. Along with the recommendation request, users provide the policy parameters, which include minimum and maximum bounds for the non-functional attributes concerning the service, and the system exploits these bounds to select and use the optimal candidate services. However, in many real-life cases, a person will accept/purchase a product or a service that exceeds the threshold(s) that initially he/she has set, e.g. if the overhead is marginal or the offer is deemed appealing, or no satisfactory service candidates are identified using the initial settings. In this paper, we present and evaluate a specialized User Interface that allows the user to review service candidates marginally exceeding the specified bounds, and consider them while making the final service selection.

}, keywords = {adaptation, business processes, Personalisation, Recommendation, Recommender Systems, UI, Web services, WS-BPEL}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {813, title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets}, journal = {Information}, volume = {13}, year = {2022}, month = {jun}, pages = {302}, abstract = {

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user{\textendash}item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system{\textquoteright}s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

}, keywords = {accuracy, collaborative filtering, Evaluation, Personalisation, Rating prediction, Recommender Systems, reliability, Sparse Datasets}, doi = {https://doi.org/10.3390/info13060302}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @article {797, title = {On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings}, journal = {SN Computer Science}, volume = {2}, year = {2021}, month = {May}, pages = {255}, abstract = {

Collaborative filtering-based recommendation systems consider users{\textquoteright} likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} phenomenon, a state where no near neighbours can be found for certain users. This phenomenon is extremely frequent in datasets where users, on average, have rated only a small percentage of the available items, which are termed as sparse datasets. This paper addresses the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} problem by proposing the virtual ratings concept and introduces an algorithm for virtual rating creation on the basis of actual ratings. The novelty behind this concept is that the introduction of the virtual ratings effectively reduces the user{\textendash}item rating matrix sparsity, thus alleviating the aforementioned problem. The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Recommender Systems, Sparse Datasets, Virtual ratings}, issn = {2661-8907}, doi = {10.1007/s42979-021-00668-8}, url = {https://doi.org/10.1007/s42979-021-00668-8}, author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Costas Vassilakis and Vasilopoulos, Dionysios} } @inbook {808, title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods}, booktitle = {Lecture Notes in Social Networks}, year = {2021}, pages = {51{\textendash}76}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.

}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks}, doi = {10.1007/978-3-030-67044-3_3}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {802, title = {A User Interface for Personalising WS-BPEL Scenarios}, booktitle = {Proceedings of the 2021 International Conference on Human-Computer Interaction: HCI in Business, Government and Organizations}, year = {2021}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Due to the huge volume of web services available, both locally and in the cloud, the performance of users and systems need significant research attention. Since WS-BPEL is the dominant language for orchestrating individual web services into business processes, by composing WS-BPEL scripts/scenarios, graphical notations facilitating WS-BPEL design can be extremely useful. Current user interfaces allow WS-BPEL designers not only to invoke selected web services, but also to explicitly ask for recommendations. Then, the user interface appends the services achieving the highest score, according to the attributes{\textquoteright} importance, set by the designer, to their WS-BPEL scenario. However, since the final selection is produced automatically, rather than relying on the designers{\textquoteright} choices, many times, from a personalisation point of view, the adaptation fails. This work reports on the design, development and user evaluation of a user interface that incorporates functionalities that support the designers{\textquoteright} selection performance, thereby upgrading the personalisation level of the WS-BPEL scenarios, as well as the success of the adaptation.

}, keywords = {Personalisation, Recommender Systems, Usability, User evaluation, user interface, Web services, WS-BPEL scenarios}, isbn = {978-3-030-77750-0}, doi = {https://doi.org/10.1007/978-3-030-77750-0_25}, author = {Dionisis Margaris and Spiliotopoulos, Dimitris and Vasilopoulos, Dionysios and Costas Vassilakis} } @article {771, title = {An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy}, journal = {IEEE Access}, volume = {8}, year = {2020}, pages = {68301{\textendash}68310}, abstract = {When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users{\textquoteright} ratings on items and users{\textquoteright} social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user{\textquoteright}s social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems}, keywords = {Performance evaluation, Recommender Systems, Social computing}, issn = {2169-3536}, doi = {10.1109/access.2020.2981567}, author = {Dionisis Margaris and Anna Kobusinska and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {779, title = {An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings}, journal = {Algorithms}, volume = {13}, year = {2020}, month = {07/2020}, pages = {174}, abstract = {

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

}, keywords = {collaborative filtering, density enrichment, derived ratings, rating prediction accuracy, rating prediction coverage, Recommender Systems, robust predictions, Sparse Datasets}, doi = {https://doi.org/10.3390/a13070174}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Gregory Karagiorgos and Costas Vassilakis} } @article {723, title = {A Fine-grained Social Network Recommender System}, journal = {Social Network Analysis and Mining}, volume = {10}, year = {2020}, abstract = {Recommender systems have greatly evolved in recent years and have become an integral part of the Web. From e-commerce sites to mobile apps, our daily routine revolves around a series of {\textquotedblleft}small{\textquotedblright} decisions that are influenced by such recommendations. In a similar manner, online social networks recommend only a subset of the massive amount of content published by a user{\textquoteright}s friends. However, the prevalent approach for the content selection process in such systems is driven by the amount of interaction between the user and the friend who published the content. As a result, content of interest is often lost due to weak social ties. In this paper, we present a fine-grained recommender system for social ecosystems, designed to recommend media content (e.g., music videos, online clips) published by the user{\textquoteright}s friends. The system design was driven by the findings of our qualitative user study that explored the value and requirements of a recommendation component within a social network. The core idea behind the proposed approach was to leverage the abundance of preexisting information in each user{\textquoteright}s account for creating interest profiles, to calculate similarity scores at a fine-grained level for each friend. The intuition behind the proposed method was to find consistent ways to obtain information representations that can identify overlapping interests in very specific sub-categories (e.g., two users{\textquoteright} music preferences may only coincide on hard rock). While the system is intended as a component of the social networking service, we developed a proof-of-concept implementation for Facebook and explored the effectiveness of our underlying mechanisms for content analysis. Our experimental evaluation demonstrates the effectiveness of our approach, as the recommended content of interest was both overlooked by the existing Facebook engine and not contained in the users{\textquoteright} Facebook News Feed. We also conducted a user study for exploring the usability aspects of the prototype and found that it offers functionality that could significantly improve user experience in popular services.}, keywords = {Content Analysis, Interest Profiles, Recommender Systems, Social Networks, Usability}, issn = {1869-5469}, doi = {10.1007/s13278-019-0621-7}, author = {Markos Aivazoglou and Antonios Roussos and Dionisis Margaris and Costas Vassilakis and Sotiris Ioannidis and Jason Polakis and Dimitris Spiliotopoulos} } @article {margaris-IJBDI-2019-a, title = {Improving collaborative filtering{\textquoteright}s rating prediction coverage in sparse datasets by exploiting the {\textquotedblleft}friend of a friend{\textquotedblright} concept}, journal = {International Journal of Big Data Intelligence}, volume = {7}, year = {2020}, chapter = {47}, abstract = {

Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbours{\textquotedblright} and recommendations for a user are based on her near neighbours{\textquoteright} ratings. However, for a number of users no near neighbours can be found, a problem termed as the {\textquotedblleft}gray sheep{\textquotedblright} problem. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time improving rating prediction quality.

}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, friend-of-a-friend, Pearson Correlation Coefficient, prediction accuracy, Recommender Systems, Sparse Datasets}, issn = {2053-1389}, doi = {10.1504/ijbdi.2020.106178}, author = {Dionisis Margaris and Costas Vassilakis} } @article {775, title = {What makes a review a reliable rating in recommender systems?}, journal = {Information Processing \& Management}, volume = {57}, year = {2020}, month = {04/2020}, pages = {102304}, abstract = {

The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user{\textquoteright}s rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users{\textquoteright} similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant.

}, keywords = {collaborative filtering, Confidence level, Feature selection, Rating prediction using textual information, Recommender Systems}, doi = {https://doi.org/10.1016/j.ipm.2020.102304}, author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos} } @article {722, title = {Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks}, journal = {Social Network Analysis and Mining}, volume = {9}, year = {2019}, month = {Oct}, pages = {64}, abstract = {One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems{\textquoteright} algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.}, keywords = {collaborative filtering, Confidence level, Rating prediction, Recommender Systems, Social Networks, Textual information, Uncertainty, Venue recommendation formulation}, issn = {1869-5469}, doi = {10.1007/s13278-019-0610-x}, url = {https://doi.org/10.1007/s13278-019-0610-x}, author = {Dionisis Margaris and Costas Vassilakis and Spiliotopoulos, Dimitris} } @article {margaris2018a, title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems}, journal = {Informatics}, volume = {5}, number = {2}, year = {2018}, month = {apr}, pages = {21 pages}, abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.}, keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks}, doi = {10.3390/informatics5020021}, url = {http://www.mdpi.com/2227-9709/5/2/21}, author = {Dionisis Margaris and Costas Vassilakis} } @article {margaris-csimq-2017, title = {Using Time Clusters for Following Users{\textquoteright} Shifts in Rating Practices}, journal = {Complex Systems Informatics and Modeling Quarterly}, volume = {13}, year = {2017}, abstract = {Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users{\textquoteright} ratings is inadequate, since it fails to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in user-user collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users{\textquoteright} rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.}, keywords = {collaborative filtering, Dynamic Average, Rating Abstention Interval, Rating Time Clusters, Ratings{\textquoteright} Timestamps, Recommender Systems}, issn = {2255-9922}, doi = {10.7250/csimq.2017-13.02}, author = {Dionisis Margaris and Costas Vassilakis} }