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- W3201260288 abstract "With the development of the Internet, people have communicated more closely. People are more likely to find more similar partners online and communicate offline. Therefore, the demand for such activity recommendation is growing, but too many activities will inevitably make users confused and difficult to choose. Personalized recommendation system can help users filter out the content that they are not interested in or have a low evaluation, and recommend the content that they may be interested in to the participating users. However, the existing research focuses on the analysis of personal participation records, while the impact of dynamic interaction between participants on user participation preferences has not been explored, and the sparse user trust matrix and other problems hinder the improvement of recommendation performance.Therefore, it is important to integrate the social relationship dynamically into the personalized recommendation system. Secondly, considering that users trust different friends in different fields, in order to infer the social relations of users in specific fields more accurately, we will use network representation technology to embed the social trust relations in specific fields into low-dimensional space. We propose a personalized dynamic recommendation method integrating high-level similarity social relationships. The results show the superiority of the proposed model by comparing with the state-of-the-art models." @default.
- W3201260288 created "2021-09-27" @default.
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- W3201260288 date "2020-12-01" @default.
- W3201260288 modified "2023-09-24" @default.
- W3201260288 title "Personalized dynamic recommendation integrating high-level similarity social relationships" @default.
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- W3201260288 doi "https://doi.org/10.1109/icisce50968.2020.00204" @default.
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