PPT-Optimizing Recommender Systems as a

Submodular Bandits Problem Yisong Yue Carnegie Mellon University Joint work with Carlos Guestrin amp Sue Ann Hong Optimizing Recommender Systems Must predict what the user finds interesting. DACs are most easily understood by examining a simplified DAC block diagram As shown in Figure 1 the architecture of a onechannel DAC consists of a resistor array each of equal value R followed by a railtorail voltage output amplifier The voltage a 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 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. 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. 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. Interaction . Effectively, yet . Infrequently, Enables . Programmers to Discover New Tools. Emerson Murphy-Hill. North Carolina State University. Gail Murphy. University of British Columbia. 1. Background. 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. Wensheng. Zhang and . Guohong. . Cao. Dynamic Convoy Tree-based Collaboration (DCTC). Constructing the Initial Convoy Tree. Apply existing root election algorithm. Other node connect to a neighbor closest to the root. Bamshad Mobasher. DePaul University. 2. What Is Prediction?. Prediction is similar to classification. First, construct a model. Second, use model to predict unknown value. Prediction is different from classification. 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. CCI, UNC-Charlotte. Research sponsored by. presented by. Zbigniew. W. Ras. WI’17, Leipzig, Germany. Project Team. Kasia. . Tarnowska. (Warsaw Univ. of . Tech., . Poland. ). Pauline Brunet. (Paris Tech.,. Infrequently, Enables . Programmers to Discover New Tools. Emerson Murphy-Hill. North Carolina State University. Gail Murphy. University of British Columbia. 1. Background. Emerson’s Problem. I was making a bunch of new user interfaces for . Performance of Recommender Algorithms on Top-N Recommendation Tasks Gabriel Vargas Carmona 22.06.12 Agenda Introduction General Overview Recommender system Evaluation RMSE & MAE Recall and precision