Fast discrete factorization machine for personalized item recommendation Abstract   Personalized item recommendation has become an essential target of Web applications, but it suffers from the efficiency problem due to a large volume of data. In particular, featurebased factorization machine models are generally limited by the vast number of feature dimensions, leading to catastrophic computation time. In this paper, we propose a Fast Discrete Factorization Machine (FDFM) method to resolve these issues by applying the hash coding technologies to factorization machine models. Specifically, it discretizes the realvalued feature vectors in the parameter model during the process of learning personalized item rankings, whereby the overall computational time can be greatly reduced. Besides, we propose convergence update rules to optimize the quantization loss of the binarization problem, which can be used in personalized ranking scenarios efficiently. Based on the evaluation in two realworld datasets, our proposed approach consistently shows better performance than other baselines, especially when using shorter binary codes.

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.