Analyzing User Preference for Social Image Recommendation
This work addresses the challenge of information overload for social media users by improving image recommendation, though it appears incremental as it builds on classical probabilistic matrix factorization.
The paper tackles the problem of social image recommendation by proposing a novel hybrid algorithm called STM that jointly analyzes image content and user preferences using sparse representation, achieving significant performance gains over state-of-the-art methods on a dataset of 0.3 million images from Flickr.
With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users' burden on the information overload. In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-item preferences, is extremely valuable for making effective recommendations. In this paper, we explore a novel hybrid algorithm termed {\em STM}, for image recommendation. STM jointly considers the problem of image content analysis with the users' preferences on the basis of sparse representation. STM is able to tackle the challenges of highly sparse user feedbacks and cold-start problmes in the social network scenario. In addition, our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationships. We evaluate our approach with a newly collected 0.3 million social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations, , with a significant performance gain over the state-of-the-art alternatives.