Improving collaborative filtering's rating prediction quality in dense datasets, by pruning old ratings

In this paper, we introduce a pruning algorithm which removes aged user ratings from the rating database used by collaborative filtering algorithms, in order to (1) improve prediction quality and (2) minimize the rating database size, as well as the rating prediction generation time. The proposed algorithm needs no extra information concerning the items' characteristics (e.g. categories that they belong to or attributes' values) and can be used with all rating databases that include a timestamp. Furthermore, we propose and validate a method for identifying the most prominent combination of a pruning algorithm and a pruning level for datasets, allowing thus to perform the selection of pruning algorithm and pruning level in an unsupervised fashion.

[1]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[2]  Panagiotis Georgiadis,et al.  A collaborative filtering algorithm with clustering for personalized web service selection in business processes , 2015, 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS).

[3]  Panagiotis Georgiadis,et al.  Query personalization using social network information and collaborative filtering techniques , 2018, Future Gener. Comput. Syst..

[4]  Dennis M. Wilkinson,et al.  Large-Scale Parallel Collaborative Filtering for the Netflix Prize , 2008, AAIM.

[5]  Charles Elkan,et al.  Optimal Thresholding of Classifiers to Maximize F1 Measure , 2014, ECML/PKDD.

[6]  Yehuda Koren,et al.  Factor in the neighbors: Scalable and accurate collaborative filtering , 2010, TKDD.

[7]  G. Karypis,et al.  Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems , 2002 .

[8]  Hong Joo Lee,et al.  Use of social network information to enhance collaborative filtering performance , 2010, Expert Syst. Appl..

[9]  Dan Wu,et al.  Toward a Robust data fusion for document retrieval , 2008, 2008 International Conference on Natural Language Processing and Knowledge Engineering.

[10]  Songjie Gong.  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering , 2010, J. Softw..

[11]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[12]  John Riedl,et al.  Collaborative Filtering Recommender Systems , 2011, Found. Trends Hum. Comput. Interact..

[13]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[14]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation.(Special Section: Recommender Systems) , 1997 .

[15]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[16]  Panagiotis Georgiadis,et al.  Recommendation information diffusion in social networks considering user influence and semantics , 2016, Social Network Analysis and Mining.

[17]  Zhi-Dan Zhao,et al.  User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[18]  Fan Yang,et al.  Modeling and broadening temporal user interest in personalized news recommendation , 2014, Expert Syst. Appl..

[19]  Jure Leskovec,et al.  Inferring Networks of Substitutable and Complementary Products , 2015, KDD.

[20]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[21]  Hans-Peter Kriegel,et al.  Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering , 2022 .

[22]  Jack J. Dongarra,et al.  Accelerating collaborative filtering using concepts from high performance computing , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[23]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[24]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[25]  Yang Song,et al.  Multi-Rate Deep Learning for Temporal Recommendation , 2016, SIGIR.

[26]  Fernando Ortega,et al.  Improving collaborative filtering recommender system results and performance using genetic algorithms , 2011, Knowl. Based Syst..

[28]  Dionisis Margaris,et al.  Pruning and aging for user histories in collaborative filtering , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[29]  Qiang Yang,et al.  One-Class Collaborative Filtering , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[30]  A. Jøsang,et al.  Filtering Out Unfair Ratings in Bayesian Reputation Systems , 2004 .

[31]  Rong Yan,et al.  Social influence in social advertising: evidence from field experiments , 2012, EC '12.

[32]  LeeHong Joo,et al.  Use of social network information to enhance collaborative filtering performance , 2010 .

[33]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.