Collaborative Filtering Recommender Systems

By J. Schafer, Dan Frankowski, Jonathan L. Herlocker, Shilad Sen. The Adaptive Web, 2007

1,069 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/c487236245a4ce3fd683a58eb14536977d62a958

How researchers see this paper

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

In Computer Networks and Communications

1. Jesús Bobadilla
2. Chang Zhou
3. Barry Smyth
4. Fernando Ortega
5. Antonio Hernando
6. Bamshad Mobasher
7. Lior Rokach
8. Bracha Shapira

In Computer Vision and Pattern Recognition

1. Fernando Ortega
2. Antonio Hernando
3. Francesco Ricci
4. Jesús Bobadilla
5. Chang Zhou
6. Jie Tang
7. Iz Beltagy
8. Arman Cohan

In Information Systems

1. Francesco Ricci
2. Hongzhi Yin
3. Fernando Ortega
4. Antonio Hernando
5. Zi-Liang Huang
6. Jesús Bobadilla
7. Daniel S. Weld
8. Iz Beltagy

Citations in the corpus

• A collaborative filtering approach to mitigate the new user cold start problem (2012, background): “The cold-start problem [48,1] occurs when it is not possible to make reliable recommendations due to an initial lack of ratings.”
• Controllable Multi-Interest Framework for Recommendation (2020, methodology): “Traditional recommendation methods mainly use collaborative filtering methods [47, 48] to predict scores between users and items.”
• Recommendation to Groups (2007, methodology): “Recommender systems for individuals often accompany each recommendation with some sort of analysis of its predicted acceptability; the analysis may range from a simple index of the system’s confidence to a complex visualization of the pros and cons of the recommended solution (see, e.g., Herlocker e”
• Recommender Systems: Introduction and Challenges (2015, background): “According to [47], ratings can take on a variety of forms:”
• Data Mining for Web Personalization (2007, methodology): “A more detailed discussion of collaborative filtering systems is provided in Chapter 9 [ 117 ].”
• Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks (2019, methodology): “In recent years, the content-based RS [21] and the collaborative filtering RS [7, 27] are two widely used methods because they can effectively approximatethe similarity between items while being simple and efficient.”
• Introduction to Recommender Systems Handbook (2011, background): “proach [93] recommends to the active user the items that other users with similar tastes liked in the past.”
• User Profiles for Personalized Information Access (2007, background): “Related research on collaborative recommender systems, discussed in Chapter 9 of this book [81], combines information from multiple users in order to provide improved information services.”
• SPECTER: Document-level Representation Learning using Citation-informed Transformers (2020, background): “Multiple users consuming the same items as one another is a classic relatedness signal and forms the foundation for recommender systems and other applications (Schafer et al., 2007).”