User intent modeling in natural language processing deciphers user requests to allow for
personalized responses. The substantial volume of research (exceeding 13,000 …

Recently, many E-commerce search models are based on Graph Neural Networks (GNNs).
Despite their promising performances, they are (1) lacking proper semantic representation of …

In the world of E-Commerce, the core task is to understand the personalized preference from
various kinds of heterogeneous information, such as textual reviews, item images and …

Personalization in Information Retrieval has been a hot topic in both academia and industry
for the past two decades. However, there is still a lack of high-quality standard benchmark …

Recommendation systems, which assist users in discovering their preferred items among
numerous options, have served billions of users across various online platforms. Intuitively …

Product search is an important service on Taobao, the largest e-commerce platform in
China. Through this service, users can easily find products relevant to their specific needs …

Recently, the growth of service platforms brings great convenience to both users and
merchants, where the service search engine plays a vital role in improving the user …

The relative success of learning to rank algorithms has raised the attention of the research
community for develo** efficient and effective ranking methods. Proposed ranking …

Text matching systems have become a fundamental service in most Searching platforms.
For instance, they are responsible for matching user queries to relevant candidate items, or …

Industrial recommendation systems typically involve a two-stage process: retrieval and
ranking, which aims to match users with millions of items. In the retrieval stage, classic …