SessionBased Recommendation with Local Invariance Sessionbased recommendation is a task to predict usersu0027 next actions given a sequence of previous actions in the same session. Existing methods either encode the previous actions in a strict order or completely ignore the order. However, sometimes the order of actions in a short subsequence, called the detailed order, may not be important, e.g., when a user is just comparing the same kind of products from different brands. Nevertheless, the highlevel ordering information is still useful because the data is sequential in nature. Therefore, a good sessionbased recommender should pay different attention to the sequential information in different levels of granularity. To this end, we propose a novel model to automatically ignore the insignificant detailed ordering information in some subsessions, while keeping the highlevel sequential information of the whole sessions. In the model, we first use a full selfattention layer with Gaussian weighting to extract features of subsessions, and then we apply a recurrent neural network to capture the highlevel sequential information. Extensive experiments on two realworld datasets show that our method outperforms or matches the stateoftheart methods.

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.