Diversity in Recommender System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : Vaishali Ghanghas , Chhavi Rana , Sunita Dhingra|
Vaishali Ghanghas , Chhavi Rana , Sunita Dhingra. "Diversity in Recommender System". International Journal of Engineering Trends and Technology (IJETT). V4(6):2344-2348 Jun 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.
Recommendation system is an activated area of research that helps to allow users to find the preferable items quickly and to avoid the possible information overloads. Recommender systems use data on past user preferences to predict possible future likes and interests. A better recommender system would offer less common papers that al so draw the user’s interest. Diversity is very much related to this aspect. It generally applies to a set of items and is related to how different the items are with respect to each other. Many diverse recommendation techniques have been developed, includi ng collaborative filtering, content - based analysis. These techniques involve presenting different types of recommendation to the users which are similar in taste. The laid down work has concluded that the diversity in a set of items can be increased at a c ost of reducing system accuracy. Though, the feature of diversity is contrasting to accuracy, many researchers have tried to bring in congruence between the two. This paper present objective functions that capture the trade - off between the goals and optimi zation problems associated with the maximization of these objectives .
1) B. Smy th and P. Mcclave (2001). Similarity vs. diversity, in the proceedings of communication of the ACM, 40(3 ): 47 – 56.
2) C. N. Ziegler, S. M. Mcnee, J. A. Konstan and G. Lausen (2005). Improving recommendation lists through topic diversification, In the Proceedin gs of the 14th international conference on World Wide Web (WWW’05), ACM, New York, USA , pp. 22 - 32.
3) C. Clarke, M. Kolla, G. Cormack, O.Vechtomova, A. Ashkan, S. Büttcher, et al. (2008). Novelty and Diversity in Information Retrieval Evaluation. 31st Intern ational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`08), (pp. 659 - 666). Singapore.
4) D. Bridge, M. H. Goker, L. Mcginty, and B. Smyth (2005). Case - Based recommender systems, In the proceedings of 3rd International confer ence on Recommender System (RecSys’05), New York, NY, USA, pp. 361 - 387.
5) D. Fleder and K. Hosanagar (2007). Recommender systems and their impact on sales diversity, In Proceedings of the 8th ACM Conference on Electronic Commerce , Montreal, Canada, pp. 192 – 1 99.
6) D. Mcsherry (2002). Diversity - conscious retrieval, In Proceedings of the 3rd European Conference on Case - Based Reasoning, Chicago, Illinois, USA, pp. 219 – 233.
7) G. Karypis (2001). Evaluation of item - based top - N recommendation algorithms, In Proceedings of the 10 th International Conference on Information and Knowledge Management of ACM SIGMIS, Chiba, Japan, pp. 247 – 254.
8) G Adomavicius and Y. Kwon (2007). Improving Aggregate Recommendation Diversity Using Ranking - Based Techniques. IEEE Transactions on Knowl edge and Data Engineering.
9) J. Breese, D. Heckerman and C. Kadie, (1998). Empirical analysis predictive algorithm for collaborative filtering. Microsoft Technical Report MSR - TR, 98(12):45 - 67 .
10) K. Goldberg, T. Roeder, D. Gupta and C. Perkins, (2001). Eigenta ste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), pp. 133 – 151.
11) L. Foner, (1997). Yenta: A multi - agent, referral - based matchmaking system. Proceedings of The First International Conference on Autonomous Agents. ACM. 16(49 ): 21 - 27.
12) M. Balabanovic and Y. Shoham (1997). Fab: Content - based collaborative recommendation, Communications of the ACM, 40(3) : 88 - 89.
13) M. Dias and P. J. Lisoba (2003). On the role of diversity in conversational recommender systems, In Workshop Proceeding s of the 5th International Conference on Case - Based Reasoning (ICCBR’03) , pp.65 - 98.
14) M. Zhang (2009). Enhancing diversity in Top - N recommendation, In Proceedings of the 3rd ACM conference on Recommender systems RecSys, Geneva, Switzerland, pp.397 - 400.
15) M. Z hang and N. Hurley (2010). Avoiding Monotony: Improving the Diversity of Recommendation Lists, In ACM RecSys, 40(6):86 - 98.
16) N. Lathia, S. Hailes, L. Capra and X. Amatriain (2010). Temporal diversity in recommender systems. In the proceedings of 33rd Intern ational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`10), Geneva, Switzerland, pp. 210 - 217.
17) P. Resnick and H. J. Varian (1997). Recommender Systems, In Proceedings of the Communications of the ACM , 40(3): 56 – 58.
18) R. Arms trong, D. Freitag, Joachims, T. Mitchell (1995). Webwatcher: A learning apprentice for the world wide web. AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments,pp. 6 - 12.
19) S. H. S. Chee, J. Han and K. Wang (2001). RecTr ee: An Effecient Collaborative Filtering Method, in the proceedings of ACM SIGCHI Conference on Information Retrieval, New York, USA, pp.141 - 151.
20) S. Sweeney, F. Crestani, and D. Losada (2008). "Show me more": Incremental Length Summarisation Using Novelty Detection. Information Processing & Management, pp. 663 - 686.
21) T. Chen and W. Han (2007). Content recommendation system base on private dynamic user profile, Machine Learning , 16 (4):12 - 18.
22) T. Sakai (2006). Evaluating evaluation metrics based on the bootstra p, in the proceedings of 29th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`06), Seattle, WA, USA, pp. 525 - 532.
23) U. Shardanand and P. Maes (1995). Social information filtering: Algorithms for automating `Word of Mouth`, In P roceedings of 1995 conference on recommender systems , Tokyo, Japan, pp. 210 - 217.
24) W. Hill, L. Stead, M. Rosenstein, and G. Furnas (1995). Recommending and evaluating choices in a virtual community of use, In Proceedings of the 1995 conferenc e on recommender systems , Madrid, Spain, pp. 194 - 201.
25) Y. Kwon (2008). Improving top - n recommendation techniques using rating variance, Proceedings of the 2008 ACM conference on Recommender Systems, Geneva, Switzerland, pp. 456 - 487.
26) Y. H. Chien and E. I. George (1999). A Bayesian Model for Collaborative Filtering and Information Gathering from Heterogeneous Distributed . Environments , In Proceedings of 4 th ACM conference on recommender systems, Vancouver, Canada, pp. 6 - 12.
Recommender system, collaborative filtering, diversity, dataset and control parameter .