Learning Dynamic Multi-attribute Interest for Personalized Product Search (2024)

Abstract: Personalized product search aims to learn personalized preferences from search logs and adjust the ranking lists returned by engines. Previous studies have extensively explored excavating valuable features to build accurate interest profiles. However, they overlook that the user’s attention varies on product attributes(e.g., brand, category). Users may especially prefer specific attributes or switch their preferences between attributes dynamically. Instead, existing approaches mix up all attribute features and let the model automatically extract useful ones from rather complex scenarios. To solve this problem, in this paper, we propose a dynamic multi-attribute interest learning model to tackle the influences from attributes to user interests. Specifically, we design two interest profiling modules: attribute-centered and attribute-aware profiling. The former focuses on capturing the user’s preferences on a single attribute, while the latter focuses on addressing the interests correlated with multi-attribute within the search history. Besides, we devise a dynamic contribution weights strategy that sends explicit signals to the model to determine the impacts of different attributes better. Experimental results on large-scale datasets illustrate that our model significantly improves the results of existing methods.

## introduction with the rapid growth of e-commerce services, online shopping has become increasingly popular. in a common e-shopping scenario, the user formulates her demands into a query and selects the items she is interested in from the list retrieved by a product search engine. however, the query could be ambiguous and have multiple meanings, which makes it difficult to capture accurate user needs. for instance, the user could enter the query word "mac" to purchase computers, but the search engine cannot distinguish the needs of computers from those of cosmetic brands. furthermore, the query could also be broad (such as "laptop"), without specifying the brands and desired features of the products the user wants to buy. personalized product search tasks address this challenge by learning interests from the user history. researchers have tried excavating features from various views for accurate interest learning. a group of studies aim at improving the features capturing ability through practical algorithms, including simple embedding-based methods @xcite , attention-based methods @xcite , or transformer-based methods @xcite . some studies pay attention to extracting features from multiple aspects. modeling the impacts of short-and long-term history @xcite @xcite has been a popular search topic. leveraging the product reviews @xcite has also achieved satisfactory results. some studies also attempt to use visual resources to model multi-modal preferences. other works @xcite @xcite explore relationships between user, items queries by constructing knowledge graphs with the help of product attributes (e.g., names, brands). however, the studies mentioned above overlook that the users' interests in different product attributes (such as brands, categories, features, etc.) are sophisticated. instead, they simply feed attribute features into the model and let it automatically learn interests from the mixed features. such a paradigm neither distinguishes the characteristics of specific attributes nor explicitly models the influences among multiple attributes. hence, we argue that previous methods do not sufficiently explore the potential within the attributes to reflect user interests. as the user behavior sequence shown in concretely, we propose a multi-attribute interest learning model (mai) for personalized product search. it includes the following four parts: to summarize, the main contributions of this paper include: ## methodology to start with, the problem could be formulated as follows. suppose that for each user, her search history h includes n purchased items , @xmath0, . . . , h n }, where h i represents i-th historical purchased items. given the current query q and the candidate target item list @xmath1, c 2 , . . .} returned by the search engine, our objective is to model a ranking probability score for each candidate item c in c based on the current query q and the purchased item sequence h. the overview of our multi-attribute interest learning model is shown in figure 1 . later, we will elaborate on the modeling details following the three stages: (1) attribute-centered interest profiling, (2) attributed-aware interest profiling and (3) multi-attribute interest fusion. ## conclusion this work proposes a product search model that dynamically captures multi-attribute interests. in this model, we explore the potential of attribute features by modeling the user's preference on parallel profiling parts, where attribute interests are modeled independently and simultaneously. for each profiling part, we feed it with item/query representations that are enhanced by specific attributes accordingly. at the interest fusion stage, we use contribution weights obtained from profiling parts to help the model determine the importance of each attribute. experiments demonstrate our model significantly outperforms existing models.