@article {869, title = {Optimizing Collaborative Filtering for Accurate Rating Predictions in Very Sparse Datasets}, journal = {Future Internet}, volume = {18}, year = {2026}, pages = {114}, abstract = {

Collaborative filtering is one of the most widely used methods for user rating prediction in recommender systems. To evaluate a collaborative filtering system, rating datasets are typically used, which comprise thousands to millions of records consisting of user{\textendash}item{\textendash}rating tuples. Initially, a similarity metric is used to quantify the closeness between each user and every other user in the dataset, typically based on the ratings that each pair of users has given to the same items. Subsequently, the K users having the largest similarity to the target user are used to produce rating predictions, which lead to recommendations. A particularly challenging case arises when the rating dataset is very sparse. In this scenario, it is difficult not only to find users with commonly rated items but also to determine the optimal similarity metric and suitable values for variable K. Setting a small value for K results in extremely low prediction coverage, leading to unsuccessful recommendations, while setting a very large K value increases memory requirements and prediction/recommendation generation time. Through a multiparameter experiment, this work aims to determine the optimal settings for rating predictions when very sparse datasets are used in collaborative filtering recommender systems.

}, keywords = {collaborative filtering, K-nearest neighbors (KNN), optimal prediction settings, prediction accuracy, Rating prediction, Recommender Systems, similarity metrics, sparse collaborative filtering, Sparse Datasets}, issn = {1999-5903}, doi = {10.3390/fi18020114}, url = {https://www.mdpi.com/1999-5903/18/2/114}, author = {Lapadaki, Sofia-Anna and Nanos, John and Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris} } @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} } @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} } @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} }