[Objective] By mining the relation characteristics between users and items, or between users and categories, this Paper extracts user preferences to optimize recommendation effect. [Methods] This paper extracts user rating and items degree attribute, mines user preferences, and puts forward the walk condition of User-Item bipartite graph; The category-User-Project-Category quadripartite graph is established by mapping User-Item-Category tripartite graph to the User-Category bipartite graph. The personalized recommendation method for user preferences through items and categories is proposed. [Results] Choosing MovieLens ratings data set as the source data, respectively comparing the experimental difference based on bipartite graph, weighted bipartite graph, tripartite graph and quadripartite graph, the results show that the Precision rate, MAE, recall rate, and coverage have been respectively optimized with this proposed method. [Limitations] Due to Movielens lack of critical textual data of users for movies, it is hard to analyze user preferences through the semantic. [Conclusions] This research analyzed user preferences through user ratings and degree attribute, it can be determined that the recommendation effect of quadripartite graph based on conditional walk is great.
(Meng Xiangwu, Liu Shudong, Zhang Yujie, et al.Research on Social Recommender Systems[J]. Journal of Software, 2015, 26(6): 1356-1372.)
Mooney R J, Roy L.Content-based Book Recommending Using Learning for Text Categorization[C]//Proceedings of the 5th ACM Conference on Digital Libraries. 2000: 195-204.
Breese J S, Heckerman D, Kadie C.Empirical Analysis of Predictive Algorithms for Collaborative Filtering[C]// Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 1998: 43-52.
Zhuang F, Luo D, Yuan N J, et al.Representation Learning with Pair-wise Constraints for Collaborative Ranking[C]// Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 2017: 567-575.
(Chen Jiemin, Li Jianguo, Tang Feiyi, et al.Combining User-Item-Tag Tripartite Graph and Users Personal Interests for Friends Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(1): 92-100.)