Internet Multimedia Search and Mining

Indexed in: EBSCO.

With the explosion of video and image data available on the Internet, desktops and mobile devices, multimedia search has gained immense importance. Moreover, mining semantics and other useful ...
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Video Recommendation

Pp. 397-414 (18)

Tao Mei and Kiyoharu Aizawa

Abstract

This chapter is intended to introduce the basic techniques for general recommender systems, as well as specific research for video recommendation, which have become interesting and important topics in video search and mining. We will first define video recommendation and survey three principle approaches to video recommendation, i.e., collaborative filtering, content-based, and hybrid approaches. We will also discuss the connection and difference between video recommendation and search. Then, we will introduce several recent exemplary systems for recommendation, including: (1) “graph-based video recommendation” which builds random work graph based on user-video pairs [5], (2) “contextual video recommendation by multimodal relevance and user feedback” which does not require a large collection of user profiles [30, 43], and (3) “consumer generated video recommendation” which discusses ranking distances for recommendation and proposes an edit-distance for ranking [22]. The first system uses collaborative filtering-based recommendation approach, while the latter two belong to content-based recommendation.

Keywords:

Video recommendation, collaborative filtering, content-based recommendation, graph-based recommendation, user generated videos, video search and mining, VideoSense, user feedback, consumer generated video, user profiles

Affiliation:

Microsoft Research Asia, China.