Neural Poisson Factorization
In this work, we focus on dealing with a sparse users' feedback matrix and short descriptions/contents of items in recommender systems. We propose the Neural Poisson factorization (NPF) model which is a hybrid of deep learning and Poisson factorization. While Poisson factorization is suitable to model discrete, massive and sparse feedback, using a deep neural network and pre trained word embeddings can learn hidden semantic in short item descriptions well. Therefore, NPF overcomes the limitation of existing models when dealing with short texts and a sparse feedback matrix. Moreover, we develop a random view algorithm based on stochastic learning for our model, in which each user is only viewed a random subset of items and his/her feedback on the subset is used to update his/her representation in each iteration. This approach is reasonable because each user can only know or view a partial subset of items when surfing a system. Extensive experiments illustrate the significant advantages of NPF over content based matrix factorization methods and others that ignore item descriptions.
IEEE Access