Web Recommendation Based on Unified collaborative Filtering

Web usage mining technique is widely used for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining can only discover usage pattern explicitly. In order to employ the users' feature and web pages' attributes to get more accuracy recommendation, we propose a unified collaborative filtering model for web recommendation which combined the latent and external features of users and web page through back propagation neural networks. In the algorithm, we employ Probabilistic Latent Semantic Analysis (PLSA) method to get latent features. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The preliminary experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained.