Abstract:

Nowadays, collaborative filtering is one of the most successful technologies applyed in information recommender systems. However, with increase of the number of users and the amount of information needed to filter, the systems′ computational complexity quickly increases, and most centralized recommender systems have to face the low scalability problem. To solve the scalability problem of the recommender systems, a distributed collaborative filtering recommendation mechanism with an unstructured P2P architecture is proposed. In the recommendation mechanism, the content of resource is  represented by a vector according to the lexical chain method, and then the user profile can be represented by a preferred resource set. In addition, with the change of the user′s interest, the proposed mechanism also utilizes dynamic neighbor peer set reformation to gain a real time personalized recommendation.

Key words:  P2P; collaborative filtering; user profile; personalized recommendation