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The recent explosive growth in the use of social networks has raised the question of how to meet the emerging demand for services that address the interests of the users. In this paper we show how considering homophily in social networks can improve video recommendation, using inferred user profiles and modeling users' interests. We propose a sociallyaware framework for video commenting, sharing and interest discovery that combines recommendation algorithms, clustering techniques, tools for video tagging and evaluation of tag semantic relatedness. The system allows to connect to friends, curate a personal profile and get video recommendations through a social network.

Brands and organizations are using social networks such as Instagram to share image or video posts regularly, in order to engage and maximize their presence to the users. Differently from the traditional advertising paradigm, these posts feature not only specific products, but also the value and philosophy of the brand, known as brand associations in marketing literature. In fact, marketers are spending considerable resources to generate their content in-house, and increasingly often, to discover and repost the content generated by users. However, to choose the right posts for a brand in social media remains an open problem. Driven by this real-life application, we define the new task of content discovery for brands, which aims to discover posts that match the marketing value and brand associations of a target brand. We identify two main challenges in this new task: high inter-brand similarity and brand-post sparsity; and propose a tailored content-based learning-to-rank system to discover content for a target brand. Specifically, our method learns fine-grained brand representation via explicit modeling of brand associations, which can be interpreted as visual words shared among brands. We collected a new large-scale Instagram dataset, consisting of more than 1.1 million image and video posts from the history of 927 brands of fourteen verticals such as food and fashion. Extensive experiments indicate that our model can effectively learn fine-grained brand representations and outperform the closest state-of-the-art solutions.