Abstract:

To mine user’s interests accurately,probabilistic latent semantic analysis (PLSA) model is firstly used to project webpage-word matrix vector into probabilistic latent semantic vector space. A method of “auto-selected similarity threshold” is proposed to get web pages similarity threshold. At last, combined with divisiory algorithms and hierarchical agglomerative clustering,a hierarchical agglomerative kmedoids clustering algorithm is proposed to realize cluster user’s interests. The experimental results show that, compared with the traditional divisiory algorithms, the hierarchical agglomerative kmedoids algorithm has a better clustering effect. Furthermore, user’s interest clustering technique can improve the efficiency of personalized recommendation and search in user’ personalized service fields.

Key words: probabilistic latent semantic analysis;autoselected similarity threshold;user’s interest points;hierarchical agglomerative kmedoids;personalized service