Deep Itembased Collaborative Filtering for TopN Recommendation Itembased Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a userxe2x80x99s profile with the items that the user has consumed, ICF recommends items that are similar to the userxe2x80x99s profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationships between items, which are insufficient to capture the complicated decisionmaking process of  :99,this article, we propose a more expressive ICF solution by accounting for the nonlinear and higherorder relationships among items. Going beyond modeling only the secondorder interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. By doing this, we can effectively model the higherorder relationship among items, capturing more complicated effects in user decisionmaking. For example, it can differentiate which historical itemsets in a userxe2x80x99s profile are more important in affecting the user to make a purchase decision on an item. We treat :99,this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higherorder item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more finegrained secondorder interaction modeling with attention network, the performance of our DeepICF method can be further improved.

Joint Neural Collaborative Filtering for Recommender Systems. We propose a JNCF method for recommender systems. The JNCF 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 useritem rating matrix. Deep interaction modeling captures nonlinear useritem interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. JNCF 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, pointwise and pairwise loss into account. Experiments on several realword datasets show significant improvements of JNCF over stateoftheart 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 HR10. NDCG10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of JNCF. Our experiments show that the JNCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to stateoftheart baselines.