PDF-(BOOS)-Recommender Systems Handbook

The explosive growth of ecommerce and online environments has made the issue of information search and selection increasingly serious users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce Correspondingly various techniques for recommendation generation have been proposed During the last decade many of them have also been successfully deployed in commercial environmentsRecommender Systems Handbook an edited volume is a multidisciplinary effort that involves worldwide experts from diverse fields such as artificial intelligence human computer interaction information technology data mining statistics adaptive user interfaces decision support systems marketing and consumer behavior Theoreticians and practitioners from these fields continually seek techniques for more efficient costeffective and accurate recommender systems This handbook aims to impose a degree of order on this diversity by presenting a coherent and unified repository of recommender systems8217 major concepts theories methodologies trends challenges and applications Extensive artificial applications a variety of realworld applications and detailed case studies are includedRecommender Systems Handbook illustrates how this technology can support the user in decisionmaking planning and purchasing processes It works for well known corporations such as Amazon Google Microsoft and ATampT This handbook is suitable for researchers and advancedlevel students in computer science as a reference. 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 : Or How to Expect the Unexpected Panagiotis Adamopoulos a n d Alexander Tuzhilin Department of Information, Operations and Management Sciences Leonard N. Stern School of Business, New York Univ 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. Explanations in recommender systems. Motivation. “The . digital camera . Profishot. . is a must-buy for you because . . . . .”. Why should recommender systems deal . with explanations at . all?. 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. Agenda. Introduction. Charactarization of Attacks. Attack models. Effectivness analysis. Countermeasures. Privacy aspects. Discussion. Introduction / Background. (Monetary) value of being in recommendation lists. 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? . 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. 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.