@booklet {846, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets (version 2)}, year = {2025}, abstract = {

In this technical report, we present the experimental findings from evaluating 15 user similarity metrics when applied in sparse collaborative filtering (CF) datasets.

}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, url = {https://soda.dit.uop.gr/sites/soda.dit.uop.gr/files/publications/technical-reports/soda-TR-24001-v2.pdf}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @article {833, title = {Experimental results for Evaluating User Similarity Metrics in Sparse Collaborative Filtering Datasets}, number = {TR-24001}, year = {2024}, month = {03/2024}, institution = {SODA lab}, type = {Technical Report}, address = {Tripolis, Greece}, keywords = {collaborative filtering, Evaluation, Sparse Datasets, User similarity metrics}, issn = {SODA-TR-24001}, author = {Kiriakos Sgardelis and Dionisis Margaris and Dimitris Spiliotopoulos 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 {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 {812, title = {Persona Finetuning for Online Gaming using Personalisation Techniques}, booktitle = {Proceedings of the 2022 HCII Conference}, year = {2022}, abstract = {

Automatic persona generation has been shown to have specific measurable benefits for application creators and users. In most situations, personas are adequately descriptive and diversified to achieve user type accuracy and coverage. For specific market segments, such as online gaming, using personas may accurately describe existing user base but not changing habit and need that are introduced by the fluidity of the offerings and the delivery methods. Changes in the ways that applications are marketed, such as new payment methods, for example, subscription models, pay-to-play and pay-to-win, payment-driven-gamification, seriously affect user needs and result in direct impact on user acceptance. This work utilises structured user needs from online gaming players to augment personas using personalisation techniques. The personas are finetuned and dediversified to result in concise personas, based on user needs that successfully convey information for creators and users alike.

}, keywords = {collaborative filtering, Data-driven methods, Diversification, Online Gaming, Persona, Personalisation, Usability evaluation, User study}, author = {Dimitris Spiliotopoulos and Dionisis Margaris and Kostas N. Koutrakis and Panos Philippopoulos 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 {803, title = {Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering}, journal = {Applied Sciences}, volume = {11}, year = {2021}, month = {sep}, pages = {8369}, abstract = {

In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users{\textquoteright} ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of {\textquoteleft}black sheep near neighbours{\textquoteright}, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

}, keywords = {black sheep users, collaborative filtering, Evaluation, rating prediction accuracy}, doi = {10.3390/app11188369}, url = {https://doi.org/10.3390/app11188369}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @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} } @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 {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} } @conference {margarisIISA_CIRPaC, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, 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 neighbors{\textquotedblright} and recommendations for a user are based on his near neighbors{\textquoteright} ratings. In order to measure similarity between users, so as to determine a user{\textquoteright}s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users{\textquoteright} or items{\textquoteright} ratings history. However users{\textquoteright} ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced {\textendash}to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we (1) propose an algorithm that considers the common item rating past in the rating prediction computation process, aiming to improve rating prediction quality, and (2) evaluate the proposed algorithm against seven widely used datasets, both dense and sparse, and considering two widely used user similarity metrics.}, keywords = {collaborative filtering, Common Item Rating Past Criterion, Cosine Similarity, Evaluation, Items{\textquoteright} Rating Sequence, Pearson Correlation Coeffi-cient}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {margaris_iisa2019_VNNs, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors}, booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)}, year = {2019}, month = {jul}, abstract = {Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by ex-ploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formu-late a recommendation for a user, her near neighbors{\textquoteright} ratings are extracted and form the basis for the recommendation. Col-laborative filtering algorithms however exhibit the problem commonly referred to as {\textquotedblleft}gray sheep{\textquotedblright}: this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The {\textquotedblleft}gray sheep{\textquotedblright} problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the {\textquotedblleft}gray sheep{\textquotedblright} problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and consid-ering two correlation metrics which are widely used in Collabo-rative Filtering research, namely the Pearson Correlation Coef-ficient and the Cosine Similarity. The results show that the pro-posed algorithm considerably leverages the capability of a Col-laborative Filtering system to compute personalized recommen-dations in the context of sparse datasets, tackling thus efficiently the {\textquotedblleft}gray sheep{\textquotedblright} problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Sparse Datasets, Virtual Near Neighbors}, author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos} } @conference {716, title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence}, year = {2019}, publisher = {ACM}, organization = {ACM}, abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as {\textquotedblleft}near neighbors" and recommendations for a user, are based on his near neighbors{\textquoteright} ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as {\textquotedblleft}Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets.}, keywords = {collaborative filtering, Cosine Similarity, Evaluation, item adoption eagerness, Pearson Correlation Coefficient, rating prediction quality}, isbn = {978-1-4503-6934-3}, doi = {10.1145/3350546.3352544}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} } @conference {Margaris_2019_asonam, title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising}, booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)}, year = {2019}, month = {aug}, abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.}, keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks}, author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis} }