Personalized Hybrid Recommendation enhanced by Visual Features

Recommender systems have become an essential part of the Web in various domains. They provide suggestions for users about which things to read, which products to buy, or which movies to watch. They try to perfectly tailor to user preferences in order to improve overall user experience. However, in some scenarios, we need to deal with the problem of insufficient amount of information about users, items, or they interactions.
Another essential part of many Web portals are images. They play an important role in extending or even replacing an information about items (such as movie posters, product photos). Users’ decision process may be led by the visual stimuli and thus, an important information about the item is hidden in the image. Moreover, these images may also contain features that can be useful during the process of user-modelling and recommendation.
In our work, we study the problem of incorporation of features extracted from the images in a recommender systems domain. We examine hybrid recommendation approaches that combine basic recommender systems techniques and incorporate images during the ranking process of output recommendations.

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