The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Collaborative personalized tweet recommendation
Abstract
Twitter has rapidly grown to a popular social network in recent years and provides a large number of real-time messages for users. Tweets are presented in chronological order and users scan the followees' timelines to find what they are interested in. However, an information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this paper, we focus on recommending useful tweets that users are really interested in personally to reduce the users' effort to find useful information. Many kinds of information on Twitter are available for helping recommendation, including the user's own tweet history, retweet history and social relations between users. We propose a method of making tweet recommendations based on collaborative ranking to capture personal interests. It can also conveniently integrate the other useful contextual information. Our final method considers three major elements on Twitter: tweet topic level factors, user social relation factors and explicit features such as authority of the publisher and quality of the tweet. The experiments show that all the proposed elements are important and our method greatly outperforms several baseline methods.
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Amazon.com recommendations: item-to-item collaborative filtering
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GroupLens: an open architecture for collaborative filtering of netnews
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BPR: Bayesian Personalized Ranking from Implicit Feedback
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Optimizing search engines using clickthrough data
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Factorization meets the neighborhood: a multifaceted collaborative filtering model
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Cited by
Bayesian Personalized Feature Interaction Selection for Factorization Machines
Chen, Y., Ren, P., Wang, Y., & de Rijke, M. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331196 · Cited by 26
Metadata
- Authors
- Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., & Yu, Y.
- Venue
- Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 661-670
- OpenAlex
- W2048508267
- Cited by
- 296
- References
- 29
- FWCI
- 26.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., & Yu, Y. (2012). Collaborative personalized tweet recommendation. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/2348283.2348372
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