Personalized image recommendation and retrieval via latent SVM based model

Authors:
Jie Sun;Songhe Feng;Wen Wang;Congyan Lang
Affiliations:
Beijing Jiaotong University, Beijing, China, P.R. China;Beijing Jiaotong University, Beijing, China, P.R. China;Beijing Jiaotong University, Beijing, China, P.R. China;Beijing Jiaotong University, Beijing, China, P.R. China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

In this paper, we investigate a problem of personalized predicting what images are likely to appear on the Web, given a query word and a database of historical images for multiple users. Inspired by recently emerging interests on personalized image search in information retrieval research, the proposed method can infer users' implicit search intent better and provide more engaging search results according to trends of Web user photos. Firstly, we collect a user historical dataset including 40 users and a panorama recommendation test dataset including 240 pictures, both of which are thoroughly divided into 5 categories, including sky, stone, plant, water, buildings. Second, we develop a predictive framework based on the latent SVM model to retrieve the most relevant images from the dataset at an individual user level, which models the relations between scene-level features and the global-level features that influence it in a globally optimal way. The experimental results on the dataset have validated the effectiveness of the proposed approaches in images recommendation.