Learning Dynamic Multi-attribute Interest for Personalized Product Search
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.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: re-ranking
Languages Studied: English, Chinese
Submission Number: 1344
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