Optimisation of combined collaborative recommender systems

AEU - International Journal of Electronics and Communications

Volumn 61, Issue 7, 2007, Pages 433-443

  Kunaver, Matevž ^a   Požrl, Tomaž ^a   Pogačnik, Matevž ^a   Tasič, Jurij ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Collaborative recommendation; Hybrid recommender systems; Personalisation; User modelling

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; DATA STRUCTURES; DATABASE SYSTEMS; OPTIMIZATION;

COLLABORATIVE RECOMMENDATION; HYBRID RECOMMENDER SYSTEMS; PERSONALISATION; RECOMMENDER TECHNIQUES; USER MODELING; USER MODELLING;

COMPUTER SUPPORTED COOPERATIVE WORK;

EID: 34249738538     PISSN: 14348411     EISSN: 16180399     Source Type: Journal    
DOI: 10.1016/j.aeue.2007.04.003     Document Type: Article

References (14)

1. Eachmovie collaborative filtering dataset. Provided by Digital Equipment Corporation. 〈http://research.compaq.com/SRC/eachmovie〉, accessed on October 2004.
2. Burke R. Hybrid recommender systems: survey and experiments. User Model User-Adap Interact 12 (2002) 331-370
3. Bezzera B, Carvalho F, Ramalho G, Zucker J-D. Speeding up recommender systems with meta-prototypes. Lecture notes in computer science, advances in artificial intelligence. Berlin: Springer; 2002. p. 227-36.
4. Zimmerman J., Kurapati K., Buczak A.L., Schaffer D., Martino J., and Gutta S. TV personalization system: design of a TV show recommender engine and interface (2004), Kluwer Academic Publishers, Dordrecht
5. Kurapati K, Gutta S. TV personalization through stereotypes. In: Proceedings of the AHÂ'2002 workshop on personalization in future TV, 2002.
6. Uchyigit G, Clark K. An agent based electronic program guide. In: Proceedings of the AHÂ'2002 workshop on personalization in future TV, 2002.
7. Salter J., and Antonopoulos N. Cinemascreen recommender agent: combining collaborative and content-based filtering. IEEE Intell Syst 21 (2006) 35-41
8. Pogacnik M, Tasic J, Tomazic S. Personalized content retrieval. In: Proceedings of the international symposium on telecommunications VITEL. Ljubljana: Electrotechnical Society of Slovenia; 2004.
9. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms.
10. Goldberg K, Roeder T, Gupta D, Perkins C. Eigenstate: a constant time collaborative filtering algorithm; 2000.
11. Masthoff J. Group modeling: selecting a sequence of television items to suit a group of viewers. User Model User-Adap Interact 14 (2004) 37-85
12. Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, Madison, USA: Morgan Kaufmann Publisher; 1998.
13. Hand D., and Smyth H.M.P. Principles of data mining (2001), The MIT Press, Cambridge
14. Raaijmakers S, den Hartog J, Baan J. Multimodal topic segmentation and classification of news video, 2002.