@article {813,
	title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets},
	journal = {Information},
	volume = {13},
	year = {2022},
	month = {jun},
	pages = {302},
	abstract = {<p>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.</p>
},
	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}
}
@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 = {<p>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.</p>
},
	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 {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 = {Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris}
}
