Content-Based Recommendation Systems

By Michael J. Pazzani, Daniel Billsus. 2007

2,884 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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. Yongfeng Zhang
3. Xu Chen
4. Tat-seng Chua
5. Charu C. Aggarwal
6. Julian McAuley
7. Philip S. Yu
8. Qiang Yang

In Computer Vision and Pattern Recognition

1. Charu C. Aggarwal
2. Qiang Yang
3. Tat-seng Chua
4. Yongfeng Zhang
5. Xiangnan He
6. Xu Chen
7. Dacheng Tao
8. Hanwang Zhang

In Information Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Xu Chen
4. Tat-seng Chua
5. Charu C. Aggarwal
6. Julian McAuley
7. Xiang Wang
8. Qiang Yang

In Recommender Systems

1. Julian McAuley
2. Xiangnan He
3. Tat-seng Chua
4. Yongfeng Zhang
5. Ruining He
6. Xu Chen
7. Qiang Yang
8. Steffen Rendle

Citations in the corpus

• Explainable Recommendation: A Survey and New Perspectives (2018, background): “Readers are encouraged to prepare with basic understandings of recommender systems, such as content-based recommendation (Pazzani and Billsus, 2007), collaborative filtering (Ekstrand et al.”
• Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “Preference matching has been richly studied in the area of recommender systems, where two kinds of approaches of interest have been developed: 1) content-based approaches [23, 29], where explicit user profiles or item information are used as features, and 2) collaborative filtering approaches where ”
• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time (2017, background): “In purely content-based recommender systems, representations for items are computed solely based on their content features [24].”
• Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks (2017, background): “Two major approach to recommender systems are collaborative (Breese et al., 1998) and content (Pazzani & Billsus, 2007) filtering techniques.”
• FISM: factored item similarity models for top-N recommender systems (2013, methodology): “In content based methods [6, 9], the features associated with users/items are used to build models.”
• Facing the cold start problem in recommender systems (2014, neutral): “…Vries, & Reinders, 2006; Rocchio, 1971; Khabbaz & Lakshmanan, 2011; Popescul, Ungar, Pennock, & Lawrence, 2001) and (b) Content-based (CB) methods (Pazzani & Billsus, 2007; Lops, de Gemmis, & Semeraro, 2011; De Gemmis, Lops, & Semeraro, 2007; Middleton, Shadbolt, & De Roure, 2004; Billsus &…”
• Learning Image and User Features for Recommendation in Social Networks (2015, methodology): “In general, recommendation methods can be classified into content-based filtering (CBF) methods and collaborative filtering (CF) methods.”
• Recommender systems based on user reviews: the state of the art (2015, methodology): “It draws on detailed representations of items to build a user profile (Balabanović and Shoham 1997; Garcia Esparza et al. 2010; Lops et al. 2011; Pazzani and Billsus 2007).”
• Collaborative Filtering Recommender Systems (2007, background): “Content-based recommendations are discussed in Chapter 10 of this book [48].”
• Recommending twitter users to follow using content and collaborative filtering approaches (2010, background): “We explore a variety of different recommendation strategies including content-based techniques [13, 6] which rely on the content of tweets, and collaborative filtering style approaches, based on the followees and followers of users, as well as a number of hybrid strategies.”
• In2Rec: Influence-based Interpretable Recommendation (2019, background): “Early approaches to personalized recommender systems mostly focused on content-based or collaborative filtering-based recommendation [3, 14, 23, 27].”
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “[44] Michael J Pazzani and Daniel Billsus.”
• Recommender Systems (2016, positive): “The works in [5, 376, 477] provide excellent overview articles on content-based recommender systems.”
• Social media recommendation based on people and tags (2010, background): “There are two prevalent approaches for building recommender systems: content-based (CB) [26] and collaborative filtering (CF) [13].”
• Interactive Visual Profiling of Musicians (2016, background): “The further profiling steps of our systems are rather comparable to content-based recommendation systems [42].”