MMM: Multi-source multi-net micro-video recommendation with clustered hidden item representation learning

Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user’s choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines. © 2019, The Author(s).