PPT-Content-based recommendation

Contentbased recommendation While CF methods do not require any information about the items it might be reasonable to exploit such information and recommend fantasy novels to people who liked fantasy novels in the past. Good experience with this system is gained The Recommendation as revised in 1992 has the aim to make it possible for non CEPT countries to participate in this licensing system The appropriate provisions for this are found mainly in the new ANNEX 3 a Basic I/O Relationship. Knowledge-based: "Tell me what fits based on my needs". Why do we need knowledge-based recommendation?. Products with low number of available ratings. Time span plays an important role. Presenters:. Kim Wilson, Career Center. Natalia Dyba, Merit Scholarships. Wednesday, October 30, 3:30-5:00pm. UW1-103. UW BOTHELL OFFICE OF MERIT SCHOLARSHIPS, FELLOWSHIPS AND AWARDS AND THE CAREER CENTER . Whom to ask for reference and letters of recommendation?. They must:. Know you well (e.g., taken for multiple classes, done a directed study with, talk outside of class). Have known you for a prolonged period of time. Abstract. Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. (SIGIR2010). IBM Research Lab. Ido. . Guy,Naama. . Zwerdling. Inbal. . Ronen,David. . Carmel,Erel. . Uziel. Social Networks and Discovery(. SaND. ). Direct entity-entity relations. Recommendation Algorithm. 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. Elise Everett, M.D.. Julie Lahiri, M.D.. Christa Zehle, M.D.. Workshop objectives:. 1. Participants . will understand the . purpose and importance . of . Letters of Recommendation (LORs) . in . the . Basic I/O Relationship. Knowledge-based: "Tell me what fits based on my needs". Why do we need knowledge-based recommendation?. Products with low number of available ratings. Time span plays an important role. S. OCIAL. N. ETWORKS. Modified from . R. . . Zafarani. , M. A. . Abbasi. , and H. Liu, . Social Networks . Mining: An Introduction. , Cambridge University Press, 2014. . Difficulties of Decision Making. S. OCIAL. N. ETWORKS. Modified from . R. . . Zafarani. , M. A. . Abbasi. , and H. Liu, . Social Networks . Mining: An Introduction. , Cambridge University Press, 2014. . Difficulties of Decision Making. IN. P2P OSN. By . Keerthi Nelaturu. Challenges with current Social Networks. Personal data left with Service Provider even when Social graph is . removed. Control of the User-generated content with Service Provider . Improve the Efficiency of YouTube Caches. D. . Krishnappa. , M. Zink, C. . Griwodz. , and P. . Halvorsen. (. MMsys. ‘13). Motivation. Each minute 72 hours of new videos are uploaded to YouTube. O. Recommendation . in . ECommerce. Amey. . Sane. CMSC-601. May 11. th. . 2011. Use of Agents in . E. Commerce. . Product Search/Identification (. eg. “. Eyes” by . amazon. ). Information Brokering.