Improving one-class collaborative filtering by incorporating rich user information

By Yanen Li, Jia Hu, Chengxiang Zhai, Ye Chen. 2010

140 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Recommender Systems

How researchers see this paper

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

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Hamed Zamani
4. Fuli Feng
5. W. Bruce Croft
6. Yuanzhi Li
7. Meng Wang
8. Jiliang Tang

In Computer Science Applications

1. Xiangnan He
2. Yue Shi
3. Yuanzhi Li
4. Flavian Vasile
5. Huan Liu

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Fuli Feng
5. Hamed Zamani
6. W. Bruce Croft
7. Xia Hu
8. Tat-seng Chua

In Recommender Systems

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. W. Bruce Croft
6. Yue Shi
7. Martha A. Larson
8. Alan Hanjalic

Citations in the corpus

• Recovery guarantee of weighted low-rank approximation via alternating minimization (2016, background): “, (Lu et al., 1997; Srebro & Jaakkola, 2003; Li et al., 2010; Eriksson & van den Hengel, 2012)), multiple heuristics for non-convex optimization such as alternating minimization have been developed, but they come with no guarantees.”
• Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation (2016, background): “Similar to [17], they also assigned weights to negative examples based on useritem content-based dissimilarity.”
• Collaborative Filtering beyond the User-Item Matrix (2014, background): “One of the most common side information sources is attribute information [Agarwal and Chen 2009; Bao et al. 2009; Gantner et al. 2010a; Koenigstein et al. 2011; Li et al. 2010; Moshfeghi et al. 2011; Shi et al. 2010a].”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Training Heuristic [69], [70], [71], [72], [73], [74], [75], [76] Sampling [30], [43], [74], [77], [78], [79] Exposure-based model [7], [8], [30], [31], [80] Others [5], [6], [81], [82], [83], [84], [85] Position Bias Click models [86], [87], [88], [89], [90], [91], [92] Propensity Score [5], [93], ”
• Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, methodology): “However, first, in [15], similarities are rather preprocessed and incorporated into the weight of WLRA, by which the formulation of one-class collaborative filtering is still used.”
• Pseudo-Relevance Feedback Based on Matrix Factorization (2016, background): “NMF has been shown to be highly effective in collaborative recommender systems [18, 44].”
• Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “Previous work has discussed its potential effect when combined with information such as user reputation (Tang et al. 2013) or user activities (Li et al. 2010).”