Personalized recommendation considering visual influences

Recommender systems are typical solution to information overload of users. Recommendations created by these systems can contain most popular items, but it was found that better solution to this problem is to personalize recommendations for users. The output of these systems is typically in the form of lists of items that we want to recommend to current user. Items on this list are usually accompanied by graphical elements such as pictures. Sadly, the influence of these elements is disregarded in the making of the recommendation. In this work, we will be exploring possible approaches for recommender systems, analysis of visual inputs of graphical elements and including these inputs in the process of creating recommendations. Currently we are working on implementing prototype, looking into the possibilities of different recommendations and looking into different ways of extracting visual influences from images.