  We propose a J-NCF method for recommender systems. The J-NCF model applies a
joint neural network that couples deep feature learning and deep interaction
modeling with a rating matrix. Deep feature learning extracts feature
representations of users and items with a deep learning architecture based on a
user-item rating matrix. Deep interaction modeling captures non-linear
user-item interactions with a deep neural network using the feature
representations generated by the deep feature learning process as input. J-NCF
enables the deep feature learning and deep interaction modeling processes to
optimize each other through joint training, which leads to improved
recommendation performance. In addition, we design a new loss function for
optimization, which takes both implicit and explicit feedback, point-wise and
pair-wise loss into account. Experiments on several real-word datasets show
significant improvements of J-NCF over state-of-the-art methods, with
improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the
MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of
HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We
also conduct experiments to evaluate the scalability and sensitivity of J-NCF.
Our experiments show that the J-NCF model has a competitive recommendation
performance with inactive users and different degrees of data sparsity when
compared to state-of-the-art baselines.
