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| 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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Abstract [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 Douban.com. 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.
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Received: 23 March 2021
Published: 23 November 2021
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| Fund:National Natural Science Foundation of China(71701134);Guangdong Basic and Applied Basic Research Foundation(2019A1515011392);Shenzhen Philosophy and Social Science Planning Project(SZ2020D015) |
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Corresponding Authors:
Liu Tingting,ORCID：0000-0002-1681-7272
E-mail: liutingting2017@email.szu.edu.cn
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