PPT-Recommender Systems:

Author : sherrill-nordquist | Published Date : 2016-03-30

Latent Factor Models Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users

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Latent Factor Models Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg Note to other teachers and users of these slides. 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 Collaborative 64257ltering the most success ful recommendation approach makes recommendations based on past transactions and feedback from consumers sharing similar interests A major problem limiting the usefulness of collaborative 64257ltering is t H. Munoz-Avila. Case-Based Reasoning. Example: Slide Creation. Repository of Presentations:. 5/9/00: ONR review. 8/20/00: EWCBR talk. 4/25/01: DARPA review. Specification. Revised. talk . 3. . Revise. Bojan Furlan, Bosko Nikolic, Veljko Milutinovic, Fellow of the IEEE. {. bojan.furlan. , . bosko.nikolic. , . veljko.milutinovic. }@. etf.bg.ac.rs. . School of Electrical Engineering, University of Belgrade, Serbia. 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. Online consumer decision making. Introduction. Context effects. Primacy/. recency. effects. Further effects. Personality and social psychology. Discussion and . summary. Literature. Introduction. Gabriel Vargas Carmona. 22.06.12. Agenda. Introduction. General Overview. Recommender. . system. Evaluation. RMSE & MAE. Recall . and. . precision. Long-. tail. Netflix. . and. . Movielens. Collaborative . www.kdd.uncc.edu. CCI, UNC-Charlotte. Research sponsored . by:. p. resented by. Zbigniew. W. Ras. CONSULTING COMPANY in Charlotte. Client 1. Client 2. Client 3. Client 4. Build . Recommender System. 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. in the Presence of Adversaries?. Bamshad Mobasher. Center for Web Intelligence. School of Computing, DePaul University, Chicago, Illinois, USA. Personalization / Recommendation Problem. Dynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests. 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.

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