Journal ArticleDOI
Recommending Flickr groups with social topic model
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TLDR
This paper presents a probabilistic latent topic model to model Flickr groups in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function.Abstract:
The explosion of multimedia content in social media networks raises a great demand of developing tools to facilitate producing, sharing and viewing media content. Flickr groups, self-organized communities with declared common interests, are able to help users to conveniently participate in social media network. In this paper, we address the problem of automatically recommending groups to users. We propose to simultaneously exploit media contents and link structures between users and groups. To this end, we present a probabilistic latent topic model to model them in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function. We demonstrate the proposed approach on the dataset crawled from Flickr.com.read more
Citations
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Journal ArticleDOI
Event detection over twitter social media streams
Xiangmin Zhou,Lei Chen +1 more
TL;DR: A novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions is proposed, and a variable dimensional extendible hash over social streams is proposed.
Proceedings ArticleDOI
Combining latent factor model with location features for event-based group recommendation
Wei Zhang,Jianyong Wang,Wei Feng +2 more
TL;DR: A method called Pairwise Tag enhAnced and featuRe-based Matrix factorIzation for Group recommendAtioN (PTARMIGAN), which considers location features, social features, and implicit patterns simultaneously in a unified model to provide better group recommendations.
Proceedings ArticleDOI
Improving User Topic Interest Profiles by Behavior Factorization
TL;DR: This work implemented and built a topic recommender predicting user's topical interests using their actions within Google+, and experimentally showed that it obtained better and cleaner signals than baseline methods, and is able to more accurately predict topic interests as well as achieve better coverage.
Journal ArticleDOI
Robust Image Hashing with Tensor Decomposition
TL;DR: A stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of the TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor.
Proceedings ArticleDOI
Probabilistic Group Recommendation Model for Crowdfunding Domains
TL;DR: A probabilistic recommendation model is proposed, called CrowdRec, that recommends Kickstarter projects to a group of investors by incorporating the on-going status of projects, the personal preference of individual members, and the collective preference of the group.
References
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Proceedings Article
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
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