PRTNets cold-start recommendation using pairwise ranking and transfer networks - Valerio, Dylan
Majority of recommendations engines use matrix factorization techniques to represent users and items as decomposed latent matrices. These matrices represent the collaborative filtering domain, where recommendations is done by consideration of the users' rating of items. The user and latent matrices create recommendations for items that have not yet been encountered by users. However, this matrix factorization approach falls short in cold-start recommendation, or recommending items with little to no ratings. This paper puts forward a novel approach of doing cold-start recommendation by using Pairwise Ranking and Transfer Networks (PRTNets). The Transfer-Network learns a nonlinear mapping function from item features to the item latent matrices. The item latent matrix is produced by another neural network, the Pairwise Ranking Network which utilizes pairwise loss functions. The Pairwise Ranking Network efficiently utilizes implicit feedback by optimizing the ranking of the recommendation list, thus enabling the recommender to achieve high average precision. PRTNets leverage on the growing literature around neural network architectural choices and are capable of scaling to large datasets through online learning. We find the optimal architecture for the PRTNets through warm-start and cold-start evaluation. Using the Pairwise Ranking Networks, we show highly personalized ranked recommendations. With the Transfer Network, we map the tag genome dataset to the item latant matrix and produce cold-start recommendations for 2014 movies and beyond. It yielded a significant margin of improvement of 0.27575 nd 0.089 average precision at k=10 over the baseline Light-FM and neighborhood averaging methods respectively.