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With Internet delivery of video content surging to an un-precedented 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 within each modality and inter-weights among different modalities by users' click-though data, as well as attention fusion function to fuse multimodal relevance together. Unlike traditional recommenders in which a sufficient collection of users' profiles is assumed available, this proposed system is able to recommend videos without users' profiles. We conducted an extensive experiment on 20 videos searched by top 10 representative queries from more than 13k online videos, reported the effectiveness of our video recommendation system.
Yang et al. (Mon,) studied this question.
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