| Summary: | Large amounts of items and users with different characteristics and preferences make personalized recommendations a problem. Many companies employ recommender systems to solve the problem of discovery and information overload where it is unreasonable for a user to go through all items to find something interesting. Recommender systems as a field of research has become popular during the past two decades. Recommendations are for many companies an important aspect of their products concerning user experience and revenue. This master’s thesis describes the development and evaluation of a recommender system in the context of a social network for sports fishing called Fishbrain. It describes and evaluates several different approaches to recommender systems. It reasons about user characteristics, user interface, and the feedback data provided by the users, for which help make recommendations. The work aims to improve user experience in the given context. All this has been implemented and evaluated, with mixed results, considering the many variables taken into account that are important to Fishbrain.
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