{"content":[{"text":"Econometrics B: Letterboxd Collaborative Filter \n Recommendation System","bbox":{"left":0.171328,"top":0.147452,"width":0.657419,"height":0.057536000000000004}},{"text":"Dan Ehrlich and Johnny Ma","bbox":{"left":0.356395,"top":0.229687,"width":0.28722899999999996,"height":0.01813200000000001}},{"text":"May 2, 2017","bbox":{"left":0.438477,"top":0.264896,"width":0.12306700000000004,"height":0.01813199999999998}},{"text":"1","bbox":{"left":0.117647,"top":0.328953,"width":0.01580899999999999,"height":0.021758}},{"text":"Introduction","bbox":{"left":0.16513,"top":0.328953,"width":0.174236,"height":0.021758}},{"text":"The Netﬂix Challenge, beginning in 2006 and ending in 2009, was a challenge to reduce \n the RMSE of Netﬂix’s rating prediction algorithm by at least 10%. The challenge involved \n over 500,000 users and 12,000 movie ratings, which was entirely made unidentiﬁable and was \n about 99% sparse, given that many users rated only a few movies. Many groups of academics, \n computer scientists, and statisticians created their own algorithms, some of which are backed \n up by mathematical theory, and used a training data-set to prepare for the test-data set, with \n each set being a 50% split by user. There were numerous approaches, ranging from naive \n averaging to ANOVA, neighborhood weighting, Latent Factor Modeling, and even Neural \n Networks and Boltzmann machines [1]. Though it seems these approaches were successful, a \n contemporary look at the implementation of these so called ”Filtering and Recommendation \n Systems” in platforms such as Netﬂix, Spotify, and other Taste based industries leaves a lot \n to be desired, especially compared to simple word of mouth recommendations. One wonders \n why these algorithms seem to be bad at returning shows that one would actually want to \n watch. Problems with the data are obvious: who, when, and why are rating Netﬂix movies? \n Can one map song listening or movie watching behavior to actual preference? Economists \n like to create models where the user has some decision or input, but the Netﬂix data has \n no observations that indicate preferences for genres, time periods, actors, directors, etc. \n beyond watching behavior. In essence, we seek to remedy these problems by implementing \n the aforementioned Latent Factor Models using a novel data source that can potentially \n ﬁll these holes. No doubt many further considerations will arise, and potential to answer \n more economic questions about taste formation or the inﬂuence on exogenous shocks (such \n as Oscar nominations/wins) will be fruitful directions to take. We measure our success in \n similar fashion; by using a training data set to run our algorithm, then testing with the \n second half of the data to reduce the RMSE in rating predictions.","bbox":{"left":0.117647,"top":0.367122,"width":0.76484,"height":0.4346229999999999}},{"text":"1","bbox":{"left":0.495219,"top":0.935507,"width":0.00955199999999995,"height":0.015109999999999957}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}