Tagommenders: connecting users to items through tags

By Shilad Sen, Jesse Vig, John Riedl. The Web Conference, 2009

335 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/7238f34443ff0af25c31b90c81dc7babe288e5f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Charu C. Aggarwal
2. Xing Xie
3. Dietmar Jannach
4. Mouzhi Ge
5. Qing He
6. Hui Xiong
7. Shulong Tan
8. Barry Smyth

In Information Systems

1. Charu C. Aggarwal
2. Xing Xie
3. Yue Shi
4. Dietmar Jannach
5. Fuzhen Zhuang
6. Hui Xiong
7. Qing He
8. Hengshu Zhu

Citations in the corpus

• Social media recommendation based on people and tags (2010, background): “Previous work has suggested tag-based recommendations, highlighting the value of tags as concise and accurate content descriptors that take into account human perception of the content [22,29].”
• Recommender Systems (2016, positive): “The method in [535] uses linear regression to integrate the tags into the recommendation process.”
• A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “Some commonly used item side information include item attributes [7], [8], [58], [58], like brands, categories; item multimedia information, like textual description [59], image features [60], audio signals [61], video features [62]; and item reviews [9], [10].”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “Tags are recognized as an information source that can be highly beneficial for improving the performance of recommender systems [Sen et al. 2009; Tso-Sutter et al. 2008; Zhen et al. 2009].”
• Music recommendation by unified hypergraph: combining social media information and music content (2010, background): “[33] address resource recommendation by inferring users’ tag preferences firstly and then compute resource item preferences based on tag preferences.”
• Recommending twitter users to follow using content and collaborative filtering approaches (2010, background): “For example, there has been some recent work [14] on the role of tags in recommender systems, and recently researchers have started to leverage user-generated reviews as a way to recommend and filter products and services.”
• Terms of a Feather: Content-Based News Recommendation and Discovery Using Twitter (2011, background): “There has been some recent work [17] on the role of tags in recommender systems, and researchers have also started to leverage user-generated reviews as a way to recommend and filter products and services.”
• How should I explain? A comparison of different explanation types for recommender systems (2014, methodology): “According to [Sen et al., 2009], approaches based on the standard TF-IDF weighting scheme however do not seem to work well in this domain.”