PDF-Do Recommender Systems Manipulate Consumer Preferences?

Author : luanne-stotts | Published Date : 2017-02-07

A Study of Anchoring Effects Gediminas Adomavicius 1 Jesse Bockstedt 2 Shawn Curley 1 Jingjing Zhang 1 1 Information and Decision Sciences Carlson School of Management University

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Do Recommender Systems Manipulate Consumer Preferences?: Transcript

A Study of Anchoring Effects Gediminas Adomavicius 1 Jesse Bockstedt 2 Shawn Curley 1 Jingjing Zhang 1 1 Information and Decision Sciences Carlson School of Management University of Minnesota 2. In57357uenc is measure of the e57355ect of user on the recommendations from recommender system In 57357uence is erful to ol for understanding the orkings of recommender system Exp erimen ts sho that users ha widely arying degrees of in57357uence in e-Commerce and Life Style Informatics: . Recommender Systems I. February 4 2013. Geoffrey Fox. gcf@indiana.edu. . . http://. www.infomall.org/X-InformaticsSpring2013/index.html. . Associate Dean for Research and Graduate Studies, School of Informatics and Computing. Problem formulation. Machine Learning. Example: Predicting movie ratings. User rates movies using one to five stars. Movie. Alice (1). Bob (2). Carol (3). Dave (4). Love at last. Romance forever. Cute puppies of love. Dietmar. . Jannach. , Markus . Zanker. , Alexander . Felfernig. , Gerhard Friedrich. Cambridge University Press. Which digital camera should I buy. ?. What is the best holiday for me and. my family. Evaluating Recommender Systems. A myriad of techniques has been proposed, . but. Which one is the best in a given application domain?. What are the success factors of different techniques?. Comparative analysis based on an optimality criterion? . Agenda. Online consumer decision making. Introduction. Context effects. Primacy/. recency. effects. Further effects. Personality and social psychology. Discussion and . summary. Literature. Introduction. Hybrid recommender systems. Hybrid: combinations of various inputs and/or composition of different mechanism. Knowledge-based: "Tell me what fits based on my needs". Content-based: "Show me more of the same what I've liked. 3.1 Consumer Preferences. 3.2 Budget Constraints. 3.3 Consumer Choice. 3.4 Revealed Preference. 3.5 Marginal Utility and Consumer Choice. 3.6 Cost-of-Living Indexes. Consumer Behavior. ●. . theory of consumer behavior . Dr. Frank McCown. Intro to Web Science. Harding University. This work is licensed under Creative . Commons . Attribution-. NonCommercial. . 3.0. Image: . http://lifehacker.com/5642050/five-best-movie-recommendation-services. and. Collaborative Filtering. 1. Matt Gormley. Lecture . 26. November 30, 2016. School of Computer Science. Readings:. Koren. et al. (2009). Gemulla. et al. (2011). 10-601B Introduction to Machine Learning. Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design. Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design. Introduction to Recommender Systems. Recommender systems: The task. Customer W. 2. Slides adapted from Jure Leskovec. Plays an Ella Fitzgerald song. What should we recommend next?. Thomas . Quella. Wikimedia Commons. Introduction. Supply and Demand Models (Ch. 2) are useful for analyzing economic questions concerning markets.. How will increasing the real wage affect output?. In these models we summed each individuals demand to obtain the market demand curve..

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