Abstract:

Aiming at tracking user’s interest in intelligent recommender systems, traditional methods such as Time Window, Forgotten Function, consider little in topic relevance when characterizing user’s interest model and could hardly make full use of historic data, leading to inaccurate in tracking user’s interest migration. Therefore, the paper proposes a new characteristic model based on paging cache, which forms user’s multiple interest domains classified by topics. This paper proposes the corresponding SIM algorithm which introduces sequence entropy difference to characterize the integral features of interest migration. Experimental results show that the proposed method provides lower mean absolute error of interest and precisely characterize user’s interest migration, so as to enhance the quality and efficiency in services.

Key words: paging cache;interest migration;sequence entropy difference;interest update