An article describing how this recommender system was created was published in Significance, the magazine of the Royal Statistical Society and American Statistical Associationhttps://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2019.01317.x

Imagine you have just finished playing a boardgame that you really enjoyed and asked those around you if they could recommend another game. Recommendations are likely to be more reliable if they come from someone who also enjoy the game you have just played, and they have a broad knowledge of other board games. If you could ask thousands of people who have played the game and collate their responses, taking account of whether they did or did not like the game, it should be possible to devise a useful recommendation algorithm. This application does this by utilising the ratings given by users of the BoardGameGeek website.

The method recommends those games that provide the biggest ratings contrast between those users who enjoyed the original game and those who either did not enjoy it or have not rated it. This contrast is scaled to lie approximately on the 0 to 10 range and shown as the User Score. The User Score is also adjusted to remove an unwanted bias that is evident between games published in a similar time period. Clearly the more users who have rated both games, the more reliable will be the User Score. A Bayesian method has been used to combine information available prior to anyone playing the game with the information contained in the ratings data. This prior information comes from the attributes of each game, such as the designer, mechanics and playing time. The Game Score, scaled to be comparable to the User Score, quantifies the prior information. The Bayesian method combines these scores, weighting them according to the relative precisions of the User and Game Scores, to give the Like Score. It is this value that is used to order the recommendations.

The Like Scores are also used to provide a Strength rating, with 3 stars implying a strong recommendation. Clearly it is not possible for the algorithm to know which aspects of the game particularly appealed to you, so there can be no guarantee that any of the recommendations will be good for you. All that can be inferred is that the games recommended are liked significantly more by those who liked the original game than those who did not like it or have not rated it. Also, games are only recommended if those who liked the original game gave it a good rating (>7) on average.

As well as computing the Like Score the Bayesian method also computes the precision of this score. A Surety rating is provided that represents this precision. A Surety of 3-stars implies high precision and so one can be confident in the reliability of the Like Score. Typically, low Surety ratings will be associated with those games that have had relatively few user ratings.

Average rating time trend

The best fitting polynomial of order 4 or less is used to show the time trend of the ratings. This is adequate to show the general shape of the time trend, but regions where the curvature is shallow (range less than 0.2) may actually be a plateau. Note: if the density of ratings at the start or end of a game's time range is low, these are removed from this plot as they could be too influential otherwise.

Linked items

Games listed by BoardGameGeek as being linked.

Like score

A "Bayesian average" of the User and Game scores weighted by their relative precision.

User score

A score based on the ratings difference between those who liked the selected game and those who did not like it or have not rated it.

Game score

A score based on the shared attributes of the recommended game and selected game, which represents the information available prior to anyone playing the game.

Strength

The Like Score is used to create a 3-star rating system denoting the strength of the recommendations.

Surety

The reliability of the Like Score value depends on the number of raters and the variation in their ratings. A 3-star rating system denotes how sure you can be in the Like Score value.