Board games have been around for thousands of years and are enjoyed by countless people. The different types of games are innumerable - from something as simple as checkers to as complex as an advanced role playing game. These games possess many qualities and attributes, including, but not limited to, number of players, complexity, age group, mechanics, and player skill level. With such a diverse set of criteria to describe games, as well as the large number of games available, one can see how it would be difficult to classify and recommend games to players that they will enjoy. Whether it is based on a set of attributes that the player desires, or the player’s history of games that they have enjoyed playing in the past, it would be very beneficial to board game enthusiasts to have access to a system that could provide such recommendations.
Expert knowledge in the problem domain of board game recommendation can come from a variety of sources. Human experts primarily include those who design board games and serious board game enthusiasts, however, in order to be comprehensive, the opinions and experiences of casual players should also be considered. The logical rules and decisions that these experts make in choosing which games to play will play a large part in a system designed to solve this particular problem.
Soruces of facts and data in regards to the problem domain can also come from non-human sources such as BoardGameGeek. This specific website contains a large, publicly accessible database of games, descriptions, and ratings. This data can be incorporated, as facts or otherwise, into a system to provide recommendations to its users.
One of the team’s members, Alex Burkhart, is actively involved in the Columbus Area Boardgaming Society. He had mentioned that with such a large library (over 1000 games), it would be very beneficial if some sort of application existed to recommend board games to prospective players. While resources such as BoardGameGeek exist, there is not a user-friendly interface available to filter and evaluate the data available to generate relevant recommendations to its users. The team decided that this problem offered a challenging, flexible, and extensible project opportunity, and would also provide a complementary service that would be frequently used and appreciated.