Smarter social collaboration at IBM research

Chi, Changyan; Liao, Q. Vera; Pan, Yingxin; Zhao, Shiwan; Matthews, Tara; Moran, Thomas P.; Zhou, Michelle X.; Millen, David R.; Lin, Ching‐Yung; Guy, Ido

doi:10.1145/1958824.1958848

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…The data are depicted from the CADAL digital library, which is the portal of the Chinese Academic Digital Associative Library. A large number of tag samples show that the shapes of the interest curves are close to the Ebbinghaus forgetting curve (Chi et al, 2011), which extrapolates the hypothesis of the exponential nature of forgetting, especially for the short-term behavior of tags. Therefore, the curves are fitted for each tag and the heat at a particular time is used as the weight to optimize the microblog preference in the microblog-tag matrix.…”

Section: Tag Optimization Based On the Interest Evolution Modelsupporting

confidence: 67%

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Sun

et al. 2015

Frontiers Inf Technol Electronic Eng

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Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.

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Section: Tag Optimization Based On the Interest Evolution Modelsupporting

confidence: 67%