Recommending based on rating frequencies: Accurate enough?

CEUR Workshop Proceedings

Volumn 606, Issue , 2010, Pages 65-70

  Gedikli, Fatih ^a   Jannach, Dietmar ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COLLABORATIVE FILTERING ALGORITHMS; LARGE PROBLEMS; MATRIX FACTORIZATIONS; PRE-PROCESSING; PREDICTIVE ACCURACY; PROBABILISTIC MODELS; SPARSE DATA SETS; STANDARD TESTS;

ALGORITHMS; COLLABORATIVE FILTERING; RECOMMENDER SYSTEMS;

RATING;

EID: 84888985881     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. [AT05] Gediminas Adomavicius and Alexander Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible exten- sions, IEEE Trans. on Knowl. and Data Eng. 17 (2005), no. 6, 734{749.
2. [HKR00] Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl, Explaining col- laborative ltering recommendations, Proc. ACM Conference on Computer Supported Cooperative Work (New York, NY, USA), 2000, pp. 241{250.
3. [JH09] Dietmar Jannach and Kolja Hegelich, A case study on the e®ectiveness of recommendations in the mobile internet, Proceedings of the 2009 ACM Con- ference on Recommender Systems (New York, NY, USA), 2009, pp. 41{50.
4. [LM05] Daniel Lemire and Anna Maclachlan, Slope one predictors for online rating- based collaborative ltering, Proceedings of the 5th SIAM International Con- ference on Data Mining (Newport Beach, CA), 2005, pp. 471{480.
5. [ZP07] Jiyong Zhang and Pearl Pu, A recursive prediction algorithm for collaborative ltering recommender systems, Proceedings of the 2007 ACM Conference on Recommender Systems (Minneapolis, MN, USA), 2007, pp. 57{64.