Cross-Platform Multi-Modal Topic Modeling for Personalized Inter-Platform Recommendation

In this paper, we investigate a novel cross- platform multimedia problem: given two platforms, Flickr and Foursquare, we conduct the recommendation between these two platforms, namely the photo recommendation from Flickr to Foursquare users and the venue recommendation from Foursquare to Flickr users. Such inter-platform recommendations enable users from one single platform to enjoy different recommendation services effectively . To solve the problem, we propose a cross- platform multi-modal topic model ( ${\rm CM}^3{\rm TM}$ ), which is capable of: 1)  differentiating between two kinds of topics, i.e., platform- specific topics only relevant to a certain platform and shared topics characterizing the knowledge shared by different platforms and 2) aligning multiple modalities from different platforms. Specifically, ${\rm CM}^3{\rm TM}$ can not only split the topic space into the shared topic space and platform-specific topic space and learn them simultaneously, but also enable the alignment among different modalities through the learned topic space. Given the location information, we applied the proposed ${\rm CM}^3{\rm TM}$ into two inter-platform recommendation applications: 1) personalized venue recommendation from Foursquare to Flickr users and 2) personalized image recommendation from Flickr to Foursquare users. We have conducted experiments on the collected large-scale real-world dataset from Flickr and Foursquare. Qualitative and quantitative evaluation results validate the effectiveness of our method and demonstrate the advantage of connecting different platforms with different modalities for the inter-platform recommendation.