Leveraging Missing Ratings to Improve Online Recommendation Systems

By Y. Ying, Fred M. Feinberg, M. Wedel. 2006

164 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Marketing

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. Barbara E. Engelhardt
2. Brandon M Stewart
3. Yong Tan
4. Junjie Wu
5. Allison June-Barlow Chaney
6. Yi-Chun Ho

In Management Science and Operations Research

1. Yong Tan
2. Junjie Wu
3. Wendy W. Moe
4. Barbara E. Engelhardt
5. Allison June-Barlow Chaney
6. Brandon M Stewart
7. David A. Schweidel
8. Yi-Chun Ho

In Marketing

1. Yong Tan
2. Junjie Wu
3. Yi-Chun Ho
4. David A. Schweidel
5. Wendy W. Moe
6. Barbara E. Engelhardt
7. Allison June-Barlow Chaney
8. Brandon M Stewart

Citations in the corpus

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Other work also considers similar notions of missingness in rating data [43, 62].”
• Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content (2012, unknown): “The marketing literature has proposed several model-based recommendation systems to predict preferences for recommended items (Ansari et al. 2000, Ying et al. 2006, Bodapati 2008).”
• Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings (2015, unknown): “Posting a Rating for a Specific Movie Whereas our rating model (3) can be used to predict poster i’s rating for movie j at time t, the poster’s decision to submit a rating and her value of rating are not independent of each other (Ying et al. 2006).”
• Disconfirmation Effect on Online Rating Behavior: A Dynamic Analysis (2014, methodology): “We develop our model based on MS which, in turn, is a generalization of Ying et al. (2006) who first propose that whether a product is rated should affect analyst's prediction of that rating.”
• Digital marketing: A framework, review and research agenda (2017, unknown): “Ying, Feinberg, and Wedel (2006) account for the endogenous selection in online recommendation ratings.”
• Marketing Analytics for Data-Rich Environments (2016, unknown): “Adequately dealing with this missing information in ubiquitous ratings-based recommendation systems can render recommendations much more effective (Ying, Feinberg, and Wedel 2006).”
• Online Product Opinions: Incidence, Evaluation, and Evolution (2012, background): “We find that the ratings environment significantly impacts an individual’s decision of whether or not to contribute a rating, resulting in a selection effect that affects the composition of the posting population.”