Personalized Recommendations Based on Sentimental Interest Community Detection

Abstract EN

Communities have become a popular platform of mining interests for recommender systems.

The semantics of topics reflect users’ implicit interests.

Sentiments on topics imply users’ sentimental tendency.

People with common sentiments can form resonant communities of interest.

In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems.

First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles.

Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users.

Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations.

Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.