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. 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. A Practical Guide for Obtaining Effective References. Need a Recommendation?. This presentation will provide a practical framework for requesting letters of recommendation.. Letters of recommendation are requested for a number of reasons, most notably:. A Practical Guide for Obtaining Effective References. Need a Recommendation?. This presentation will provide a practical framework for requesting letters of recommendation.. Letters of recommendation are requested for a number of reasons, most notably:. 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. 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. Criteo. Simon . Dollé. RecSys. . FR, . December. 1. st. , . 2015. We buy. Ad spaces. We buy. Ad spaces. We sell. Clicks. We buy. Ad spaces. We sell. Clicks. that convert. We buy. Ad spaces. We sell. Danielle Lee . April 20, 2011. Three basic recommendations . Collaborative Filtering. : exploiting other likely-minded community data to derive recommendations. Effective, Novel and Serendipitous recommendations . 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. Chrysalis Wright & Mark Rubin. Abstract. . This study examined the relationship between sexual content in music and sexual cognitions and risk among emerging adults in the United States and Australia. Music content was examined via lyrics, music videos, and social media posts of popular music artists. It was hypothesized that there would be a positive association between sexual content in music and sexual cognitions and risk. Sexual content in music lyrics, videos, and social media was assessed using content analysis of the top artists rated by participants in the United States and Australia. Findings indicated variations in sexual content based on music genre and location, and that music lyrics, videos and social media posts all contain sexual content. Results from hierarchical regression analyses indicated that sexual lyrical content, sexual content in music videos, and sexual references in the social media posts of artists were related to negative sexual cognitions for both samples. This trend was also found for the degree of sexual risk for both samples. While findings point to the direction of a universal impact of the association between sexual content in music and sexual cognitions and degree of sexual risk, they also highlight trends in these relationships across countries.. 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 . 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 )..