The current system uses 188 categorical attributes to recommend video games to existing users. The biggest disadvantage of this approach is the large amount of computational time required to build the tables the recommender requires. The numerous categorical features also makes adding numerical features (such as game price) to the system challenging; the influence of the categorical features will outweigh any impact the numerical features could have on the recommendations. To correct both issues, we will attempt to reduce the number of attributes the recommender using K-Means clustering.<\/p>\n\n\n\n

The raw, unprocessed video game metadata contains 380+ attributes. We observed from looking at the data for several video games that some attributes tend to appear together with other attributes. This gave me the idea to try to group these attributes based on the likihood the attributes will appear together. We will do this by constructing a square matrix that contains the conditional probability of observing any two attributes in a video game. We use this matrix to cluster the game attributes.<\/p>\n\n\n

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The code snippet below shows the construction of the probability matrix. We iterate through the each game in the steam games dataset and construct a dictionary containing the sets of games that have each attribute.<\/p>\n\n\n\n

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