Table-top games have proven to enhance the lives of people of all ages. From children fostering the ability to focus to adults reducing their risk of developing Alzheimers disease, games continue to play a significant role in people’s success in life. Recent prevalence in table-top games have increased production and sales for the game industry, thus providing a large variety of table-top game options to users. The volume of table-top options available to users, however, is problematic as buying new, unfamiliar games is a risk, since purchase cannot guarantee play satisfaction, and 100% refunds are not warrantied if game components have been tampered with. Existing websites such as Amazon and Barnes & Noble recommend table-top games to users, but their methodology is mainly based on consumer purchase patterns which neglect game characteristics in their non-personalized recommendations. These characteristics, which include topic relevance, complexity, and game category, can significantly affect the satisfaction level of game play when players experiment with new table-top games. In order to assist users in finding games of interest to play and enrich the player’s gameplaying experience, we have developed PeGRec (Personalized Game Recommender), a novel software system that recommends the latest and most personally intriguing table-top games for users. We show in a series of evaluation tests that PeGRec’s recommendations are more personalized and accurate in rankings according to user interests than the ones provided by Amazon’s and Barnes & Nobles’ game recommenders, respectively.
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