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 users' 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 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 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. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.  Nowadays, e-commerce has become very popular with the flourishing of the Internet. Its convenient access to an enormous variety of goods enables people to shop almost all products at home. When users on e-commerce websites like Amazon 1 intend to purchase a product, they usually pick their desired ones among millions of items by searching. The standard scenario for the online product search is that a user submits a query, and then the search engine returns a ranked list of products relevant to the given query. Typically, queries submitted by users comprise only a few keywords (e.g., white T-shirt for men), which are usually too short or ambiguous to express the users' needs precisely, resulting in unsatisfactory search results. Besides, users' preferences on products could be very diverse (due to different backgrounds, i.e., age, gender, income) or strongly affected by the current contexts (e.g., season, location). Thereby it is not appropriate to return the same search results to different users for the same query. In the light of this, considering the user's personal intentions under the current contexts and aiming to return relevant products to the given query, the so-called personalized product search, plays a pivotal role in meeting the user's current shopping needs. The prerequisite for a good personalized product search engine is to accurately model the user preferences and effectively integrate them with the current query. It is well recognized that there are two types of user preferences [3, 63] : long-term ones and short-term ones. The former refers to the user's inherent and relatively stable (evolving slowly) purchasing bias, such as favorite colors, preferred fashion designs, fitting sizes and consumption level, which is imperceptibly influenced by the user's personal backgrounds, like age, upbringing, marriage, education and income. By contrast, the short-term user preference conveys user's purchasing intention in a relatively short period. It is affected by incidentally transient events, such as new product release, season change and special personal occasions like birthday [63] , which can be inferred from the user's recently purchased products. Compared to the long-term user preference, the short-term one changes more frequently and drastically. Traditional approaches to product search [17] [18] [19] 59 ] often employ simple matching between the queries and products without harnessing the user's specific attributes. They hardly characterize the user specificity, let alone the heterogeneity. It thus often leads to sub-optimal performance due to the user's diverse expectations. In view of this, personalization becomes quite necessary for the product search. In fact, personalized search has been widely studied in literature over the past few years. Thereinto, the study of personalized web search incorporating the user preference is the most related sub-direction to our work, and it can be roughly divided into two categories: short-term session-based 2 and long-term profile-based web search. Approaches in the first category [15, 29, 55] capture the short-term user preference from search sessions. Nevertheless, different from the web search whereby a session often contains plenty of queries and rich click results, the session data in the product search 3 are usually too sparse to train a good personalized
Then Guo et al. REF proposed an Attentive Long Short-Term Preference model (ALSTP) for personalized product search by considering the long-term and short-term user preferences using two attention networks.
Attentive Long Short-Term Preference Modeling for Personalized Product Search
TOIS
Computer Science