PPT-Content-based recommendation

Author : pasty-toler | Published Date : 2016-08-13

Contentbased recommendation While CF methods do not require any information about the items it might be reasonable to exploit such information and recommend fantasy

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Content-based recommendation: Transcript

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. 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. 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. 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. 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. Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design. 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. 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 . 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.

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