Complementary Context-Enhanced Concept Lattice Aware Personalized Recommendation
Wenqing Huang, Fei Hao, Guangyao Pang, Yifei Sun · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · 2021
With the rapid development of information technology, the huge amount of information is booming exponentially, and it becomes a key challenge to find the information users required from the massive information. To tackle this challenge, this paper focuses on developing a novel personalized recommendation approach based on complementary context-enhanced concept lattice. To be specific, the proposed approach constructs the concept lattices for both formal context and complementary context of user-item interactive data, and then adopts a Formal Concept Analysis based association rule recommendation algorithm for obtaining two recommendation result sets separately, and ultimately analyzes the acquisition of the final resulting recommendation for each of these two recommendation result sets under different situations. Taking movie recommendation as an illustrative example, more accurate user-preferred movie recommendation can be achieved. Compared with the traditional personalized recommendation algorithms, it is confirmed that our recommendation approach is more reasonable and effective in the real recommendation systems.