An interest propagation based movie recommendation method for social tagging system

Li, Haibo; Feng, Shi; Yu, Ge

doi:10.1109/icmlc.2017.8107754

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…To solve the problem of sparsity in social tags, Zhang et al (2013) built a ternary interaction graph and then applied random walk to explore the transfer relationship between users and resources. Liu et al (2017) propose a hybrid method that combines the collaborative filtering (CF) method and graph-based interest propagation for movie recommendation.…”

Section: Related Workmentioning

confidence: 99%

Topic optimization–incorporated collaborative recommendation for social tagging

Pan

Zeng

Ling

2022

DTA

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PurposeWith the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.Design/methodology/approachCombining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.FindingsExperimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.Originality/valueWith the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

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Section: Related Workmentioning

confidence: 99%