PPT-Prediction Modeling for Personalization & Recommender S

Author : conchita-marotz | Published Date : 2016-12-02

Bamshad Mobasher DePaul University 2 What Is Prediction Prediction is similar to classification First construct a model Second use model to predict unknown value

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Prediction Modeling for Personalization & Recommender S: Transcript

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. A major Conversant research study evaluating attitudes plans and actions of both brand marketers and agencies reveals a shift in focus among marketers in 2014 As pressure for driving results becomes stronger marketers are moving away from piecemeal Deepak . Agarwal. Yahoo! Research. Yucheng Low . Carnegie Mellon University. Alexander J. . Smola. Yahoo! Research. Information Flood. Personalization. 3. Golf Reader. Tech. Reader. Can we provide personalization to new users?. 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. . . MARKETING TECHNOLOGY. . SIMPLIFIED.. WE KNOW THE IMPACT OF WEB . IT’S ONLY STARTING . LET’S DRILL DOWN ON AUTOMOTIVE SALES AND MARKETING … . “Mind the consideration gap”. AND PERSONALISATION. 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. (. Chapter 9) . Ken Koedinger. 1. Personalization Principle. Which is better for student learning?. Conversational style of instruction. Formal style of instruction. Example. : . “. You. should be very careful if . 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. Danielle Lee . April 20, 2011. Three basic recommendations . Collaborative Filtering. : exploiting other likely-minded community data to derive recommendations. Effective, Novel and Serendipitous recommendations . 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. Gabriel Vargas Carmona. 22.06.12. Agenda. Introduction. General Overview. Recommender. . system. Evaluation. RMSE & MAE. Recall . and. . precision. Long-. tail. Netflix. . and. . Movielens. Collaborative . 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.,. Quick tips: Principles. Simon Kingsnorth. Author of Digital Marketing Strategy: An integrated approach to digital marketing. User Experience. Ensure your roles are defined clearly. Who owns UX, design, business requirements?. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:.

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