Context-Based User Typicality Collaborative Filtering Recommendation
Jinzhen Zhang, Qinghua Zhang, Zhihua Ai, Xintai Li · Human-Centric Intelligent Systems · 2021
Since contextual information significantly affecting users’ decisions, it has attracted widespread attention. User typicality indicates the preference of user for different item types, which could reflect the preference of user at a higher abstraction level than the items rated by user, and can alleviate data sparsity. But it does not consider the impact of contextual information on user typicality. This paper proposes a novel context-based user typicality collaborative filtering recommendation algorithm (named CBUTCF), which combines contextual information with user typicality to alleviate the data sparsity of context-aware collaborative filtering, and extracts, measures and integrates contextual information. First, the items are clustered and classified into different item types. For different users, the significance of contextual information for different item types is defined and measured via knowledge granulation. Then, the contextual information is combined with user typicality to measure the context-based user typicality; subsequently, the ‘neighbor’ users are determined. Finally, the unknown ratings under a single context are predicted, and the unknown ratings under multi-context are predicted according to the weighted summation of the significance of contextual information. The experimental results demonstrate that CBUTCF can effectively improve the accuracy of recommendation and increase coverage.