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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (4): 117-125    DOI: 10.11925/infotech.2096-3467.2018.0662
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A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm
Yiwen Zhang1(),Chenkun Zhang1,Anju Yang1,Chengrui Ji1,Lihua Yue2
1Institute of Information Engineering, Anhui Xinhua University, Hefei 230088, China
2School of Computer, University of Science and Technology of China, Hefei 230026, China
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[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.

Key wordsRecommendation System      Quadripartite Graph      Conditional Walk      Personalized Recommendation     
Received: 21 June 2018      Published: 29 May 2019

Cite this article:

Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm. Data Analysis and Knowledge Discovery, 2019, 3(4): 117-125.

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