Contextual Collaborative Filtering Recommendation Model Integrated with Drift Characteristics of User Interest

User interest will drift with the change of context, cognitive psychology, and so on, which leads to inaccurate recommendation. In order to address this issue and the traditional recommendation problems such as cold start and data sparsity, this study proposed a novel contextual collaborative filtering recommendation model. First, the reasons for drift of user interest from the perspective of motivation were analyzed, and this study designed a mechanism based on Maslow's hierarchy of needs to analyze the information category and information behavior corresponding to the hierarchy of users' needs. Then, a novel user interest determination algorithm was proposed based on ontology and hidden Markov. Second, this study introduced the concept of user activity and proposed a user activity computational method integrated with context to solve the cold start and data sparsity problems. Finally, the research proposed a dynamic collaborative filtering recommendation algorithm integrated with user activity to diversify the content of candidate recommendation selectively. By monitoring users' feedback and the learning rules of interest drift, this method can discover drift of user interest and make some adaptation actively. The experimental results showed that this model, which integrates with the drift characteristics of user interest, can effectively improve the adaptability to the drift of user interest, and that it has higher accuracy compared with other recommendation methods.

Automated summary

This study proposed a novel contextual collaborative filtering recommendation model to address user interest drift issues. By analyzing reasons for drift and designing mechanisms, introducing user activity concepts, and proposing algorithms to improve accuracy, the model effectively deals with traditional recommendation problems.