Internet Recommendation Systems

By Asim Ansari, Skander Essegaier, Rajeev Kohli. 2000

778 citations recorded by the source corpus.

Subfields: Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4f12b7e2637d2395f4095622747c25e123936ab8

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Citations in the corpus

Leveraging Missing Ratings to Improve Online Recommendation Systems (2006, methodology): “Model-based methods that have been used to generate product recommendations include the mixture model (Chien and George 1999), the hierarchical Bayes model (Ansari, Essegaier, and Kohli 2000), factor analysis (Canny 2002), and Bayesian network models (Breese, Heckerman, and Kadie 1998).”
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs (2007, background): “[3] and Maes et al.”
E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact (2007, methodology): “Collaborative-filtering RAs, on the other hand, mimic “word-of-mouth” recommendations (Ansari et al. 2000) and use the opinions of like-minded people to generate recommendations (Ansari et al. 2000; Maes et al. 1999).”
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (2009, background): “Reviews are provided in Breese et al. (1998) and Adomavicius and Tuzhilin (2005).”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Another possible approach to producing multdimensional recommendations would be to deploy the hierarchical Bayesian method presented in [ 5 ], which can be extended from two to multdimensional case as follows: Instead of considering the two-dimensional case, as defined in (15), where user characteri”
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction (2016, methodology): “Traditionally the model ﬁtting approaches for GLMix have relied on Gibbs sampling [3, 10] or trying to integrate the log likelihood over the random eﬀect and obtaining the marginal maximum likelihood estimates of the hyper-parameters [4, 8].”
Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings (2006, background): “Research and practical applications of the recommendation system have been widely reported (e.g., [ 3 , 6, 29, 43]).”
Digital marketing: A framework, review and research agenda (2017, positive): “Ansari and Mela (2003) showed that emails with customized design and content can increase website traffic.”
Modeling Customer Lifetime Value (2006, methodology): “Recently, some researchers have used Bayesian approach for creating more powerful recommendation systems (Ansari, Essegaier, and Kohli 2000).”
Understanding Customer Trust in Agent-Mediated Electronic Commerce, Web-Mediated Electronic Commerce, and Traditional Commerce (2004, background): “Definitions of customer trust in agent-mediated e-commerce Prior research on customer trust in agent-mediated e-commerce is scarce, although there are quite a few studies on computer agent usage in agent-mediated e-commerce [24,59,71,84], on computer agent design [ 3 ,103], on the effects of compute”