Pp. 397-414 (18)
Tao Mei and Kiyoharu Aizawa
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 , (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 . The first system uses collaborative filtering-based recommendation
approach, while the latter two belong to content-based recommendation.
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
Microsoft Research Asia, China.