A Recurrent Neural Network Based Recommendation System
6 Recommendation systems play an extremely important role in e commerce; 7 by recommending products that suit the taste of the consumers, e commerce 8 companies can generate large profits. The most commonly used 9 recommender systems typically produce a list of recommendations through 10 collaborative or content based filtering; neither of those approaches take 11 into account the content of the written reviews, which contain rich 12 information about user's taste. In this paper, we evaluate the performance of 13 ten different recurrent neural network (RNN) structure on the task of generating 14 recommendations using written reviews. The RNN structures we study include 15 well know implementations such as Multi stacked bi directional Gated 16 Recurrent Unit (GRU) and Long Short Term Memory (LSTM) as well as novel 17 implementation of attention based RNN structure. The attention based structures 18 are not only among the best models in terms of prediction accuracy, they also 19 assign an attention weight to each word in the review; by plotting the attention 20 weight of each word we gain additional insight into the underlying mechanisms 21 involved in the prediction process. We develop and test the recommendation 22 systems using the data provided by Yelp Data Challenge. 23