论文信息 - Performance and Quality Assessment of Similarity Measures in Collaborative Filtering Using Mahout

Performance and Quality Assessment of Similarity Measures in Collaborative Filtering Using Mahout

Abstract Recommendation systems use knowledge discovery and statistical methods for recommending items to users. In any recommendation system that uses collaborative filtering methods, computation of similarity metrics is a primary step to find out similar users or items. Different similarity measuring techniques follow different mathematical approaches for computation of similarity. In this paper, we have analyzed performance and quality aspects of different similarity measures used in collaborative filtering. We have used Apache Mahout in the experiment. In past few years, Mahout has emerged as a very effective and important tool in the area of machine learning. We have collected the statistics from different test conditions to evaluate the performance and quality of different similarity measures. Categories and Subject Descriptors C.4 [Performance of Systems]: Measurement Techniques, Performance attributes General Terms Performance, Measurement

Saikat Bagchi | Saikat Bagchi

[1] Gediminas Adomavicius,et al. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[2] Nava Tintarev,et al. Rate it again: increasing recommendation accuracy by user re-rating , 2009, RecSys '09.

[3] John Riedl,et al. Recommender systems in e-commerce , 1999, EC '99.

[4] Sean Owen,et al. Mahout in Action , 2011 .

[5] Gerhard Friedrich,et al. Recommender Systems - An Introduction , 2010 .

[6] Alan Said,et al. Comparative recommender system evaluation: benchmarking recommendation frameworks , 2014, RecSys '14.

[7] John Riedl,et al. An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[8] Jonathan L. Herlocker,et al. Evaluating collaborative filtering recommender systems , 2004, TOIS.

[9] Xavier Amatriain,et al. The wisdom of the few: a collaborative filtering approach based on expert opinions from the web , 2009, SIGIR.

[10] Paul Resnick,et al. Recommender systems , 1997, CACM.

[11] Lior Rokach,et al. Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[12] Guy Shani,et al. Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.