Background (4): Web Personalization and User Profile

Personalization mechanisms in literature can be divided to three categories. These mechanisms try to predict user interest in a particular item [2].
Demographic: similarity of current item properties with items that users liked in the past [1].
Content-based: based on the similar properties of the items that user liked in the past [1].
Collaborative: based on the rating patterns of similar users (the choices of people that liked similar objects as the current users are recommended) [3].

Advantages and disadvantages of these mechanisms are discussed in [4]. Demographic filtering (recommended) is more adaptable to the preference changes comparing to content-based filtering but requires some information which sometimes user is not willing to provide [2]. Collaborative filtering is a good alternative to demographic filtering, as it does not rely on information about the users and the items.


Reference
[1] C. Basu, H. Hirsh, W. Cohen, Recommendation as classification: using social and content-based information in recommendation, in: Proceedings of AAAI-98, Menlo Park, CA, USA, 1998, pp. 714–720.

[2] Stegers R., Fekkes P. and StuckenschmidtH. MusiDB: A personalized search engine for music, Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 2006, Pp 267-275

[3] D. Goldberg, D. Nichols, M. Oki Douglas Terry, Using collaborative filtering to weave an information tapestry, in: Communications of the ACM, vol. 35, issue 12, ACM Press, New York, USA, 1992, pp. 61–70


[4] R. Burke, Hybrid recommender systems: survey and experiments, in: User Modeling and User-Adapted Interaction, vol. 12, issue 4, Springer, 2002, pp. 331–370.