Forgetting techniques for stream-based matrix factorization in recommender systems

Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users’ preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users’ preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.

[1]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

[2]  Fabio A. González,et al.  TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model , 2003, Third IEEE International Conference on Data Mining.

[3]  Òscar Celma,et al.  Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space , 2010 .

[4]  Min Zhao,et al.  Online evolutionary collaborative filtering , 2010, RecSys '10.

[5]  Ee-Peng Lim,et al.  Modeling Temporal Adoptions Using Dynamic Matrix Factorization , 2013, 2013 IEEE 13th International Conference on Data Mining.

[6]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[7]  A KonstanJoseph,et al.  The MovieLens Datasets , 2015 .

[8]  Lars Schmidt-Thieme,et al.  Towards Optimal Active Learning for Matrix Factorization in Recommender Systems , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[9]  Myra Spiliopoulou,et al.  Selective Forgetting for Incremental Matrix Factorization in Recommender Systems , 2014, Discovery Science.

[10]  Kush R. Varshney,et al.  Collaborative Kalman Filtering for Dynamic Matrix Factorization , 2014, IEEE Transactions on Signal Processing.

[11]  Geoff Hulten,et al.  Catching up with the Data: Research Issues in Mining Data Streams , 2001, DMKD.

[12]  Myra Spiliopoulou,et al.  Forgetting methods for incremental matrix factorization in recommender systems , 2015, SAC.

[13]  P. Massa,et al.  Trust-aware Bootstrapping of Recommender Systems , 2006 .

[14]  João Gama,et al.  Issues in evaluation of stream learning algorithms , 2009, KDD.

[15]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

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

[17]  Domonkos Tikk,et al.  Scalable Collaborative Filtering Approaches for Large Recommender Systems , 2009, J. Mach. Learn. Res..

[18]  Patrick Seemann,et al.  Matrix Factorization Techniques for Recommender Systems , 2014 .

[19]  T DumaisSusan,et al.  Using linear algebra for intelligent information retrieval , 1995 .

[20]  Myra Spiliopoulou,et al.  Semi-supervised Learning for Stream Recommender Systems , 2015, Discovery Science.

[21]  J. Shaffer Multiple Hypothesis Testing , 1995 .

[22]  Dimitris Plexousakis,et al.  Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms , 2005, ISMIS.

[23]  Ivan Koychev,et al.  Gradual Forgetting for Adaptation to Concept Drift , 2000 .

[24]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[25]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[26]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[27]  João Gama,et al.  Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback , 2014, UMAP.

[28]  Fabio A. González,et al.  Performance of Recommendation Systems in Dynamic Streaming Environments , 2007, SDM.

[29]  Alípio Mário Jorge,et al.  Forgetting mechanisms for scalable collaborative filtering , 2012, Journal of the Brazilian Computer Society.

[30]  Ivan Koychev,et al.  Adaptation to Drifting User's Interests , 2000 .

[31]  Yi Ding,et al.  Collaborative filtering on streaming data with interest-drifting , 2007, Intell. Data Anal..

[32]  Yaroslav O. Halchenko,et al.  Open is Not Enough. Let's Take the Next Step: An Integrated, Community-Driven Computing Platform for Neuroscience , 2012, Front. Neuroinform..

[33]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[34]  Michael R. Lyu,et al.  Online learning for collaborative filtering , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[35]  Òscar Celma Herrada Music recommendation and discovery in the long tail , 2009 .

[36]  João Gama,et al.  A survey on learning from data streams: current and future trends , 2012, Progress in Artificial Intelligence.

[37]  Yifan Hu,et al.  Collaborative Filtering for Implicit Feedback Datasets , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[38]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[39]  Catarina Miranda,et al.  Incremental Collaborative Filtering for Binary Ratings , 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.