MARS: Memory AttentionAware Recommender System In this paper, we study the problem of modeling usersu0027 diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model usersu0027 various interests, we propose a Memory Attentionaware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep adaptive user representations. Trained in an endtoend fashion, MARS adaptively summarizes usersu0027 interests. In the experiments, MARS outperforms seven stateoftheart methods on three realworld datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.

Attentive Long ShortTerm Preference Modeling for Personalized Product Search Ecommerce users may expect different products even for the same query, due to their diverse personal preferences. It is well known that there are two types of preferences: longterm ones and shortterm ones. The former refers to usersxe2x80x99 inherent purchasing bias and evolves slowly. By contrast, the latter reflects usersxe2x80x99 purchasing inclination in a relatively short period. They both affect usersxe2x80x99 current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long ShortTerm Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long and shortterm user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the shortterm as well as longterm user preferences are more relevant to the current query. This unique design enables our model to capture usersxe2x80x99 current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long and shortterm user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several stateoftheart product search methods in terms of different evaluation metrics.