Abstract
With the popularization of social networks, increasing numbers of users choose to use Weibo to get information. However, as the number of users grows, the information on Weibo is also multiplying, making it increasingly difficult for users to find the right information they are interested in. Therefore, how to recommend high-quality friends to follow the Weibo is one of the focuses of studies in Weibo-based personalized services. Based on existing Weibo social networking topologies and content-based hybrid recommendation algorithms, the study proposed a hybrid recommendation algorithm based on social relations and time-sequenced topics, which has been verified using Real Sina Weibo datasets. The results show that the improved hybrid recommendation algorithm works well and achieves better mean average precision (MAP) than existing other counterparts.
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This work is supported by China National Natural Science Foundation under Grant 61602519.
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Zhang, Y., Tu, Z. & Wang, Q. TempoRec: Temporal-Topic Based Recommender for Social Network Services. Mobile Netw Appl 22, 1182–1191 (2017). https://doi.org/10.1007/s11036-017-0864-3
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DOI: https://doi.org/10.1007/s11036-017-0864-3