PPT-Social/Collaborative Filtering

Outline Recap SVD vs PCA Collaborative filtering aka Social recommendation kNN CF methods classification CF via MF MF vs SGD vs Dimensionality Reduction and Principle Components Analysis Recap. es Neal Lathia Dept of Computer Science University College of London Gower Street London WC1E 6BT UK nlathiacsuclacuk Josep M Pujol Telefonica Research Via Augusta 177 Barcelona 08021 Spain jmpstides Haewoon Kwak KAIST Computer Science Dept Kuseongdo 1600 Amphitheatre Pkwy Mountain View CA 94043 abhinandangooglecom Mayur Datar Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 mayurgooglecom Ashutosh Garg Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 ashutoshgooglecom Shyam Raja 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 Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Social Search. Social search . Communities. of users . actively participating. in the search process. Goes beyond classical search tasks. Deep Packet Inspection. Artyom. . Churilin. Tallinn University of Technology 2011. Web filtering & DPI. Web filtering (content control) . is a way control . what content is permitted to a . user. . Teacher Professional . Development. Building Resilience in Children and Young People. Using Collaborative Learning Activities. What are Collaborative Learning Strategies?. ‘Collaborative learning strategies’ are dialogic in nature and involve student-to-student interaction, rather than just teacher-student interaction. Agenda. Collaborative Filtering (CF). Pure CF approaches. User-based nearest-neighbor. The Pearson Correlation similarity measure. Memory-based and model-based approaches. Item-based nearest-neighbor. Zhenwei. He & . Cen. . Zhe. . Qiao. School of Informatics. University of Edinburgh. Outline. Introduction. Modeling Markets as Markov random field. Mining from Collaborative Filtering System(CFS). social. interactions for children with . Autism. Spectrum Disorders through . multi-touch . tablet applications. Juan Pablo Hourcade, Natasha Bullock-Rest and Thomas Hansen. Department of Computer Science. Kinan Halloum . 1. Presented paper. 2. Deep content-based music recommendation . by van den Oord et al. NIPS 2013. Outline. Music Recommendation. Collaborative filtering. Weighted Matrix Factorization. Studies . Looking Ahead. Why might critics fear an emphasis on teamwork?. What are the two types of grouping that this chapter will focus on?. Can You?. identify the differences and similarities between cooperative groups and collaborative groups?. . Sharada Gade. 11:40 to 12:00, 30 September, ISCAR2014, Sydney, Australia. 2. Overview. Research domains. CHAT perspectives. Mathematics education. Practitioner inquiry. Case study with one teacher. All slides ©Addison Wesley, 2008. Social Search. Social search . Communities. of users . actively participating. in the search process. Goes beyond classical search tasks. Key differences. Users interact with the system. 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.