E-Commerce Recommendation Applications

By J. Schafer, J. Konstan, John Riedl, Ron Kohavi, Foster J. Provost. 2004

1,842 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, 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 Information Systems

1. Sophie Ahrens
2. Yongfeng Zhang
3. Xu Chen
4. Francesco Ricci
5. Gediminas Adomavicius
6. Li Chen
7. Alexander Tuzhilin
8. Xiangnan He

In Marketing

1. J. Konstan
2. Francesco Ricci
3. Li Chen
4. John Riedl
5. Duen-Ren Liu
6. Ya-Yueh Shih
7. Tariq Mahmood
8. Sophie Ahrens

In Recommender Systems

1. Yongfeng Zhang
2. Li Chen
3. Francesco Ricci
4. Gediminas Adomavicius
5. John Riedl
6. J. Konstan
7. Sophie Ahrens
8. Xu Chen

Citations in the corpus

• Degenerate Feedback Loops in Recommender Systems (2019, background): “Recommender systems are increasingly used to provide users with personalized product and information offerings (Ben Schafer, Konstan, and Riedl, 2001; Lu et al., 2015; Covington, Adams, and Sargin, 2016).”
• Explainable Recommendation: A Survey and New Perspectives (2018, background): “highlighted as the visual explanations; Sharma and Cosley (2013) and Quijano-Sanchez et al.”
• LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation (2020, background): “Personalized recommendation has become an increasingly vital component in many online applications, such as e-commerce portals and content sharing applications [20, 28].”
• Mining interesting knowledge from weblogs: a survey (2005, background): “A survey on existing commercial recommendation systems, implemented in e-commerce Web sites, is presented in [64].”
• Collaborative Filtering Recommender Systems (2011, methodology): “The toolbox of recommender techniques has also grown beyond collaborative filtering to include content-based approaches based on information retrieval, bayesian inference, and case-based reasoning methods [132, 139].”
• A Taxonomy of Recommender Agents on the Internet (2003, methodology): “Schafer et al., in particular, present a taxonomy of recommender systems in the e-commerce field, classifying the techniques used into three dimensions (Schafer et al. 2001).”
• Recommender Systems: Introduction and Challenges (2015, background): “This is the reason why [48] refers to collaborative filtering as “people-to-people correlation.”
• Recommender Systems (2012, background): “1 As discussed in [18], recommender systems not only help decide which products to offer to an individual customer, they also increase cross-sell by suggesting additional products to the customers and improve consumer loyalty because consumers tend to return to the sites that best serve their needs…”
• Web Usage Mining as a Tool for Personalization: A Survey (2003, background): “Hitherto, Web personalization has been related mainly with recommender systems and customized Web sites for newspapers, electronic shops, etc. (Schafer et al., 2001), (Pretschner and Gauch, 1999).”
• Introduction to Recommender Systems Handbook (2011, methodology): “The largest majority of the recommender systems developed so far follow a “pull” model [94]; where the user originates the request for a recommendation.”
• Recommender system application developments: A survey (2015, background): “In addition, there are recommender system survey papers on specific application domains, such as e-commerce recommender systems [16, 17] and e-learning recommender systems [18].”