PPT-Deep content-based Music Recommendation

Kinan Halloum 1 Presented paper 2 Deep contentbased music recommendation by van den Oord et al NIPS 2013 Outline Music Recommendation Collaborative filtering Weighted Matrix Factorization. Derek . Gossi. CS 765. Fall 2014. The Big Problem. How do we make better music recommendations?. . The Big Problem. How do we make better music recommendations?. . Personalized recommendations. Anonymous recommendations based on similarity. 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. Content-based 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. Content-based 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. ManetS. Adeela Huma. 02/02/2017. Introduction - MANETs. MANETs- Mobile ad hoc networks . lacks infrastructure and . central . authority to . establish and . facilitate communication . in the . network. -. Xiaoqian. Liu. May 2, 2015. 1. When the music is over, turn out the lights.. - . The Doors, “When the Music’s Over”. 2. What’s the mainstream. 3. Top Artists on “The Hot 100, Billboard Charts Archive”. (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. Deepak Agarwal. dagarwal@yahoo-inc.com. Stanford Info Seminar. . 17. th. . Feb, 2012 . Recommend applications. Recommend search queries. Recommend news article. Recommend packages:. Image. Title, summary. 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 . -. Xiaoqian. Liu. May 2, 2015. 1. When the music is over, turn out the lights.. - . The Doors, “When the Music’s Over”. 2. What’s the mainstream. 3. Top Artists on “The Hot 100, Billboard Charts Archive”. Recommendation Systems. April 13, 2022. Mohammad Hammoud. Carnegie Mellon University in Qatar. Today…. Last Wednesday’s Session:. Ranked Retrieval– Part II. Today’s Session:. Recommendation Systems. Collin Donaldson. What is it?. World Wide Web content that is not part of the Surface Web and is indexed by search engines.. Most content that is not readily accessible using standard means (i.e. search engines )..