@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}
}
@article {SODA-TR-19003,
	title = {Experimental results for considering Item Adoption Eagerness Information in Collaborative Filtering{\textquoteright}s Rating Prediction},
	year = {2019},
	month = {07/2019},
	publisher = {Software and Database Systems Lab, University of the Peloponnese},
	type = {Technical report},
	address = {Tripolis},
	abstract = {In this technical report, we present the experimental findings from applying an algorithm that considers Item Adoption Eagerness Information in the rating prediction formulation process, in order to increase rating prediction quality in Collaborative Filtering (CF).
To this end, the algorithm is applied to seven datasets, which are widely used in recommender system (RS) research. The results indicate that the above algorithm introduces considerable prediction accuracy gains. 
},
	url = {https://soda.dit.uop.gr/?q=TR-19003},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {margaris2018tldks,
	title = {Enhancing Rating Prediction Quality Through Improving the Accuracy of Detection of Shifts in Rating Practices},
	journal = {Lecture Notes in Computer Science},
	volume = {10940 (LNCS)},
	number = {XXXVII},
	year = {2018},
	pages = {151{\textendash}191},
	publisher = {Springer Berlin Heidelberg},
	abstract = {The most widely used similarity metrics in collaborative filtering, namely the Pearson Correlation and the Adjusted Cosine Similarity, adjust each individual rating by the mean of the ratings entered by the specific user, when computing similarities, due to the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as lenient and subsequently become stricter or vice versa; hence by relying on a single mean value per user, we fail to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we present a novel algorithm for calculating dynamic user averages, i.e. time-in-point averages that follow shifts in users{\textquoteright} rating practices, and exploit them in both user-user and item-item collaborative filtering implementations. The proposed algorithm has been found to introduce significant gains in rating prediction accuracy, and outperforms other dynamic average computation approaches that are presented in the literature.},
	doi = {10.1007/978-3-662-57932-9_5},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@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-tldks,
	title = {Enhancing User Rating Database Consistency Through Pruning},
	journal = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIV},
	volume = {LNCS, volume 10620},
	number = {Special issue on Consistency and Inconsistency in Data-centric Applications},
	year = {2017},
	pages = {33{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by the interests and likings of people may change: people listen to different singers or even different types of music, watch different types of movies, read different types of books and so on. Due to this type of changes, an amount of inconsistency is introduced in the database since a portion of it does not reflect the current preferences of the user, which is its intended purpose. In this paper, we present a pruning technique that removes old aged user behavior data from the ratings database, which are bound to correspond to invalidated preferences of the user. Through pruning (1) inconsistencies are removed and data quality is upgraded, (2) better rating prediction generation times are achieved and (3) the ratings database size is reduced. We also propose an algorithm for determining the amount of pruning that should be performed, allowing the tuning and operation of the pruning algorithm in an unsupervised fashion. The proposed technique is evaluated and compared against seven aging algorithms, which reduce the importance of aged ratings, and a state-of-the-art pruning algorithm, using datasets with varying characteristics. It is also validated using two distinct rating prediction computation strategies, namely collaborative filtering and matrix factorization. The proposed technique needs no extra information concerning the items{\textquoteright} characteristics (e.g. categories that they belong to or attributes{\textquoteright} values), can be used in all rating databases that include a timestamp and has been proved to be effective in any size of users-items database and under two rating prediction computation strategies. Note: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author{\textquoteright}s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.},
	keywords = {Collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/978-3-662-55947-5_3},
	url = {https://link.springer.com/article/10.1007\%2Fs11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-soca-17,
	title = {Exploiting Internet of Things information to enhance venues{\textquoteright} recommendation accuracy},
	journal = {Service Oriented Computing and Applications},
	volume = {todb},
	year = {2017},
	month = {aug},
	pages = {393{\textendash}409},
	publisher = {Springer Nature},
	abstract = {In this paper, we introduce a novel recommendation algorithm, which exploits data sourced from web services provided by the Internet of Things in order to produce more accurate venue recommendations. The proposed algorithm provides added value for the web services offered by the Internet of Things and enhances the state-of-the-art in this algorithm category by taking into account (a) web of things data regarding the contexts of the user and the context of the venues to be recommended (restaurants, movie theatres, etc.), such as the user{\textquoteright}s geographical position, road traffic and weather conditions, (b) qualitative aspects of the venues, such as price, atmosphere or service, (c) the semantic similarity of venues and (d) the influencing factors between social network users, derived from user participation in social networks. The combination of these features leads to more accurate and better user-targeted recommendations. We also present a framework which incorporates the above characteristics, and we evaluate the presented algorithm, both in terms of performance and recommendation quality.},
	keywords = {collaborative filtering, Evaluation, Internet of Things, performance, Recommendation Applications, Social Networks, Web services},
	doi = {10.1007/s11761-017-0216-y},
	url = {https://link.springer.com/article/10.1007/s11761-017-0216-y},
	author = {Dionisis Margaris and Costas Vassilakis}
}
