D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling

Exponential growth of social media consumption demands e‚ective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To e‚ectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We €rst mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. Œen, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a largemargin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an endto-end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the e‚ectiveness of D-Sempre in user interests-social contents modeling, we construct a TwiŠer dataset and conduct extensive experiments on it. As a result, D-Sempre e‚ectively integrates the multimodal data from heterogeneous social media feeds and captures the hidden semantic correlations between users’ interests and social contents.