Online video recommendation based on multimodal fusion and relevance feedback

With Internet delivery of video content surging to an unprecedented level, video recommendation has become a very popular online service. The capability of recommending relevant videos to targeted users can alleviate users’ efforts on finding the most relevant content according to their current viewings or preferences. This paper presents a novel online video recommendation system based on multimodal fusion and relevance feedback. Given an online video document, which usually consists of video content and related information (such as query, title, tags, and surroundings), video recommendation is formulated as finding a list of the most relevant videos in terms of multimodal relevance. We express the multimodal relevance between two video documents as the combination of textual, visual, and aural relevance. Furthermore, since different video documents have different weights of the relevance for three modalities, we adopt relevance feedback to automatically adjust intra-weights w...

• Type: Conference
• Year: 2007
• Where: CIVR
• Authors: Bo Yang, Tao Mei, Xian-Sheng Hua, Linjun Yang, Shi-Qiang Yang, Mingjing Li
• Keywords: CIVR 2007, Multimodal Relevance, Video Documents, Video Recommendation