Dr Frank McCown Intro to Web Science Harding University This work is licensed under Creative Commons Attribution NonCommercial 30 Image httplifehackercom5642050fivebestmovierecommendationservices ID: 563606 Download Presentation
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.
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.
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.
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? .
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.
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.
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?.
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.
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