With the fast development of Internet, many recommender systems have emerged in e-commerce applications to support the product recommendation. However, most centralized recommender systems based on collaborative filtering can't work effectively when large number of users require to them. In P2P networks, this paper proposes a PRBOCF (Product Recommendation Based On Collaborative Filtering), which is a scalable mechanism to recommend products in distributed way. In PRBOCF, as two main parts of product information, image and text are weighted respectively and their features are represented by one vector. For increasing the quality of representing the text of product according to the Vector Space Model, WordNet v2.0 is employed to deal with the relationship of words in the text. Then a peer's preference is represented by a feature space consisting of all the vectors of its saved products information. For acquiring the recommender systems scalable and best quality of recommendation, PRBOCF makes product recommendation by searching for neighbor peers with similar preference through local information of recent ratings. Finally, the simulation results are discussed.
['Fuyong Yuan', 'Jian Liu', 'Chunxia Yin', 'Yulian Zhang', 'Nan Shen']
A Novel Collaborative Filtering Mechanism for Product Recommendation in P2P Networks