board game recommendation post

Author: JStech

_posts/2015-02-01-board-game-recommendations.md

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+---
+layout: layout
+title: A Board Game Recommendation Engine
+---
+
+I decided to create a recommendation engine for board games, using data scraped
+from [boardgamegeek.com][bgg]. My code is available [on GitHub][gh], but be
+warned---I managed to get my IP blocked from BGG while collecting data. In my
+defense:
+
+ 1. They have a [wiki page about data mining][dm] the site, and
+ 1. I was making 1 request per second, and downloaded less than 100MB of data.
+    I could do that by hand.
+
+My apologies to the Board Game Geek Powers That Be for my insolent behavior. I
+promise to never do it again. Or I'll at least do it (even) slower.
+
+### The data
+
+Before I was blocked, I managed to scrape the ratings lists from 1,700 users who
+had rated at least one game. Over 1,000 users had rated at least 10 games. There
+are almost l2,000 games in the database, although I only used the 1,000
+most-rated games for the user similarity measure (described below). All the web
+scraping code is in Python 3, and I also used Python to parse the XML and write
+the matrix of ratings to [HDF5][hd].
+
+### The engine
+
+A recommendation engine needs to do two things:
+
+   1. Find other users who like the things you like, and
+   1. Figure out what they really like that you've not yet rated.
+
+However, the problem can be considered more simply as predicting the rating a
+user would give to a game they've not yet rated. Once you've made those
+predictions, you can simply recommend the games with the highest predicted
+ratings.
+
+I followed the outline described [in this post][dp], and implemented the
+prediction engine in R. Users are represented as a vector of ratings, and the
+similarity of two users is the cosine between their vectors. Then, a rating is
+predicted by taking the average of other users' ratings, weighted by their
+similarity with the user in question.
+
+The issue of missing data is not addressed in the post linked above. I wrote a
+modified cosine function to handle NA values. By default, the cosine function
+returns NA if any of the inputs are NA, but I wanted it to project onto the
+largest subspace with no missing data for a given pair of users, and calculate
+the cosine of the projected vectors.
+
+### Results
+
+For testing purposes, my recommendation function predicts ratings for all games
+for a given user. It ignores the user's true ratings in the process, which has
+no effect on making recommendations, since you wouldn't recommend a game the
+user has already rated, but allows honest comparisons between true and predicted
+ratings for testing purposes.
+
+Here are the 20 games with the highest predicted rating for a particular user,
+along with the mean and standard deviation of their ratings.
+
+
+    > r[1:20,]
+       game predictedRating rating
+    1    21        8.248609    8.0
+    2    91        8.132131    9.0
+    3     6        8.110919    8.5
+    4    59        8.105064    8.5
+    5     4        8.026672    9.0
+    6     7        7.979619    9.0
+    7    36        7.939304    9.0
+    8    48        7.935049    9.0
+    9    52        7.922401    8.0
+    10   29        7.888088    9.5
+    11   45        7.859293    9.0
+    12   62        7.833052    9.0
+    13  153        7.832915    9.0
+    14   19        7.832199    7.5
+    15  492        7.816715    NaN
+    16   80        7.812988    NaN
+    17   81        7.763401    9.0
+    18    8        7.755711    9.0
+    19  139        7.750971    8.5
+    20  132        7.749858    NaN
+
+    > mean(r$rating[!is.na(r$rating)])
+    [1] 6.914315
+    > var(r$rating[!is.na(r$rating)])^0.5
+    [1] 1.489091
+
+The list is sorted by predicted rating, so it's satisfying to see high true
+ratings at the top of the list.
+
+### Next steps
+
+There are a couple things I'd like to do.
+
+ 1. Do some quantitative analysis of my results. I'm not ready to declare
+    success as things stand currently---I need to consider what objective
+    measures of success are appropriate.
+ 1. Do some qualitative analysis of my results: get some board game geek friends
+    to send me a list of their favorite games, so I can make recommendations to
+    them, and see what they think of the results.
+ 1. Use the id-to-name mappings that I stored away, so the results are a bit
+    more interesting to look at.
+ 1. Try another method for comparison, such as a k-nearest neighbors algorithm.
+ 1. Get myself unblocked.
+
+[bgg]: http://www.boardgamegeek.com
+[gh]: https://github.com/JStech/bggrec
+[dm]: http://boardgamegeek.com/wiki/page/Data_Mining
+[hd]: http://www.hdfgroup.org/HDF5/
+[dp]: http://www.dataperspective.info/2014/05/basic-recommendation-engine-using-r.html

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