Abstract
With the vast amount of video data uploaded to the Internet every day, how to analyze user interests and recommend videos that they are potentially interested in is a big challenge. Most video recommender systems limit the content to metadata associated with videos, which could lead to poor recommendation results since the metadata is not always available or correct. On the other side, visual content of videos contain information of different granularities, from a whole video, to portions of a video, and to an object in a video, which are not fully explored. This extra information is especially important for recommending new items when no user profile is available. In this paper, a novel recommendation framework, called VideoTopic, that targets at cold-start items is proposed. VideoTopic focuses on user interest modeling and decomposes the recommendation process into interest representation, interest discovery, and recommendation generation. It aims to model user interests by using a topic model to represent the interests in the videos and then discover user interests from user watch histories. A personalized list is generated to maximize the recommendation accuracy by finding the videos that most fit the user's interests under the constraints of some criteria. The optimal solution and a practical system of VideoTopic are presented. Experiments on a public benchmark data set demonstrate the promising results of VideoTopic.
