Collaborative Filtering beyond the User-Item Matrix

By Yue Shi, Martha A. Larson, Alan Hanjalic. 2014

858 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research

How researchers see this paper

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

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Tat-seng Chua
5. Jun Xu
6. Hang Li
7. Le Wu
8. Hamed Zamani

In Management Science and Operations Research

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Le Wu
6. Kun Zhang
7. W. Bruce Croft
8. Hamed Zamani

Citations in the corpus

• Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, methodology): “[28], and the references therein, for a recent review on such hybrid CF methods.”
• A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems (2017, methodology): “…overcome the cold start and data sparsity problems, it is inevitable for CF-based methods to exploit additional sources of information about the users or items, also known as the side information, and hence hybrid CF methods have gained popularity in recent years (Shi, Larson, and Hanjalic 2014).”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “The first part focuses on the specific topics or directions, such as side information utilization in collaborative filtering [7], cross-”
• Advances and Challenges in Conversational Recommender Systems: A Survey (2021, background): “, social networks, knowledge graphs) to better interpret user intention [151].”
• FHI Oxford Technical Report # 2018-2 Predicting Human Deliberative Judgments with Machine Learning (2018, background): “This is the idea behind collaborative filtering [39], where a person’s rating of a song is predicted from the ratings of similar people.”
• Recommender Systems (2016, neutral): “information for recommendations, such as social, temporal, side information, or contextual data, is provided in [544].”
• Deep Learning for Matching in Search and Recommendation (2018, positive): “laborative filtering principle has been proposed (Shi et al., 2014).”
• Research-paper recommender systems: a literature survey (2015, background): “For instance, the idea behindCF can be realizedwith user-basedCF [221], contentboosted CF [222], and various other approaches [223].”
• Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (neutral): “Recommender systems benefit from new information that goes beyond the user-item matrix [16][17][18][19].”
• Pseudo-Relevance Feedback Based on Matrix Factorization (2016, background): “The predictions are usually done based on the similarity between the users’ behavior [29, 34].”
• Online Learning: A Comprehensive Survey (2018, background): “Several survey paper provides detailed introduction to the regular CF techniques [180, 181].”
• A Survey on Session-based Recommender Systems (2019, background): “[94] comprehensively analyzed the recently proposed algorithms for collaborative filteringbased RSs and discussed the future challenges in the area.”
• Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios (2020, methodology): “Among them, collaborative filtering (CF) is a well known and widely adopted recommendation technique [2], [3], [4], [5], [6], [7].”
• Caching at the wireless edge: design aspects, challenges, and future directions (2016, background): “[9] Y.”
• Diversity, Serendipity, Novelty, and Coverage (2016, methodology): “Smyth and McClave [2001] applied the technique in a case-based recommender where, given a user’s query, the database of cases is searched to retrieve the most relevant cases. In this setting, rel(i) represents the similarity between the user’s query and a case, while dist(i, j) is the complement of ”