  E-commerce 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: long-term ones and short-term ones. The former refers to
user' inherent purchasing bias and evolves slowly. By contrast, the latter
reflects users' purchasing inclination in a relatively short period. They both
affect users' 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 Short-Term Preference model, dubbed
as ALSTP, for personalized product search. Our model adopts the neural networks
approach to learn and integrate the long- and short-term user preferences with
the current query for the personalized product search. In particular, two
attention networks are designed to distinguish which factors in the short-term
as well as long-term user preferences are more relevant to the current query.
This unique design enables our model to capture users' current search
intentions more accurately. Our work is the first to apply attention mechanisms
to integrate both long- and short-term user preferences with the given query
for the personalized search. Extensive experiments over four Amazon product
datasets show that our model significantly outperforms several state-of-the-art
product search methods in terms of different evaluation metrics.
