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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (10): 94-102    DOI: 10.11925/infotech.2096-3467.2021.0291
Current Issue | Archive | Adv Search |
Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes
Yang Chen,Chen Xiaohong,Wang Chuhan,Liu Tingting()
College of Management, Shenzhen University, Shenzhen 518060, China
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[Objective] This study proposes an improved recommendation model based on the users’ preferences for fine-grained attributes, aiming to address the data sparsity issues of the exisiting algorithms. [Methods] First, we constructed models for the project-attribute relationship and user-attribute preference. Then, we built simliar clusters for users and projects respectively. Finally, we used the collaborative filtering algorithm to generate recommendation lists based on user or project clusters. [Results] We examined the new method with dataset from Compared with the suboptimal models, the proposed approach significantly improved the Precision and Recall of the recommendation tasks (upto 19.7% and 44.6% respectively). [Limitations] More research is needed to further improve the representation and modeling of multi-dimensional fine-grained attributes. [Conclusions] The proposed model could effectively represent users’ interests and improve the performance of recommendation.

Key wordsRecommendation Algorithms      Collaborative Filtering      Item Attribute Preference      Clustering     
Received: 23 March 2021      Published: 23 November 2021
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(71701134);Guangdong Basic and Applied Basic Research Foundation(2019A1515011392);Shenzhen Philosophy and Social Science Planning Project(SZ2020D015)
Corresponding Authors: Liu Tingting,ORCID:0000-0002-1681-7272     E-mail:

Cite this article:

Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes. Data Analysis and Knowledge Discovery, 2021, 5(10): 94-102.

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The Algorithm Flow Chart
Recommendation Method Based on Users
Recommendation Method Based on Items
电影数 用户数 评分数 演员数 稀疏度
1 153 2 706 5 000 3 098 99.84%
Dataset Description
Performance of Two Strategies Under Different Clustering Parameter K
Comparison of Different Alogorithms
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