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Personalized video recommendation through tripartite graph propagation

Authors:

Bisheng Chen,

Jingdong Wang,

Qinghua Huang,

Tao MeiAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1133 - 1136

https://doi.org/10.1145/2393347.2396401

Published: 29 October 2012 Publication History

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Abstract

The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest to them. Video search, such as Google, Youtube, Bing, is a popular way to help users to find desired videos. However, it is still very challenging to discover new video contents for users. In this paper, we address the problem of providing personalized video suggestions for users. Rather than only exploring the user-video graph that is formulated using the click-through information, we also investigate other two useful graphs, the user-query graph indicating if a user ever issues a query, and the query-video graph indicating if a video appears in the search result of a query. The two graphs act as a bridge to connect users and videos, and have a large potential to improve the recommendation as the queries issued by a user essentially imply his interest. As a result, we reach a tripartite graph over (user, video, query). We develop an iterative propagation scheme over the tripartite graph to compute the preference information of each user. Experimental results on a dataset of 2,893 users, 23,630 queries and 55,114 videos collected during Feb. 1-28, 2011 demonstrate that the proposed method outperforms existing state-of-the-art approaches, co-views and random walks on the user-video bipartite graph.

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Lu YHuang YZhang SHan WChen HFan WLai JZhao ZWu F(2024)Multi-trends Enhanced Dynamic Micro-video RecommendationArtificial Intelligence10.1007/978-981-99-8850-1_35(430-441)Online publication date: 4-Feb-2024
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Index Terms

Personalized video recommendation through tripartite graph propagation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Published In

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
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Xian-Sheng Hua
Microsoft, USA
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Rong Yan
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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 29 October 2012

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MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Deldjoo YSchedl MKnees P(2024)Content-driven music recommendation: Evolution, state of the art, and challengesComputer Science Review10.1016/j.cosrev.2024.10061851(100618)Online publication date: Feb-2024
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Lu YHuang YZhang SHan WChen HFan WLai JZhao ZWu F(2024)Multi-trends Enhanced Dynamic Micro-video RecommendationArtificial Intelligence10.1007/978-981-99-8850-1_35(430-441)Online publication date: 4-Feb-2024
https://doi.org/10.1007/978-981-99-8850-1_35
Jayaramu HMaji SYahia H(2024)Personalized Multi‐User‐Based Movie and Video Recommender SystemSupervised and Unsupervised Data Engineering for Multimedia Data10.1002/9781119786443.ch7(149-175)Online publication date: Apr-2024
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Personalized Video Recommendation through Graph Propagation

A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks

Personalized video recommendation based on viewing history with the study on YouTube

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