DMFP: A Dynamic Multifaceted FineGrained Preference Model for Recommendation The time signals behind a useru0027s historical behaviors are important for better inferring what she prefers to interact with at the next time. For the attentionbased recommendation methods, relative position encoding and time intervals division are two common ways to model the time signal behind each behavior. They either only consider the relative position of each behavior in the behavior sequence, or process the continuous temporal features into discrete category features for subsequent tasks, which can hardly capture the dynamic preferences of a user. In addition, although the existing recommendation methods have considered both longterm preference and shortterm preference, they ignore the fact that the longterm preference of a user may be multifaceted, and it is difficult to learn a useru0027s finegrained shortterm preference. In this paper, we propose a Dynamic Multifaceted Finegrained Preference model (DMFP), where the multihops attention mechanism and the featurelevel attention mechanism together with a vertical convolution operation are adopted to capture usersu0027 multifaceted longterm preference and finegrained shortterm preference, respectively. Therefore, DMFP can better support the next item recommendation. Extensive experiments on three realworld datasets illustrate that our model can improve the effectiveness of the recommendation compared with the stateoftheart methods.

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.