Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI). However, customers are often unsure or ...

Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and ...

Traditional retrieval systems are hardly adequate for Legal Research, mainly because only returning the documents related to a given query is usually insufficient. Legal documents are extensive, and we posit that generating questions about them and ...

Offer alignment is a key step in a product knowledge graph construction pipeline. It aims to align retailer offers of the same product for better coverage of product details. With the rapid development of online shopping services, the offer alignment ...

How to best represent words, documents, queries, entities, relations, and other variables in information retrieval (IR) and related applications has been a fundamental research question for decades. Early IR systems relied on the independence assumptions ...

Information retrieval (IR) systems have become an essential component in modern society to help users find useful information, which consists of a series of processes including query expansion, item recall, item ranking and re-ranking, etc. Based on the ...

Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle with undesired repetitive outcomes that need resolution. We make two ...

Authorship attribution is a task that aims to identify the author of given pieces of writing. Authorship representation learning using neural networks has been shown to work in open-set environment settings with hundreds of thousands of authors. However, ...

Information diffusion prediction is fundamental to understand the structure and organization of the online social networks, and plays a crucial role to blocking rumor spread, influence maximization, political propaganda, etc. So far, most existing ...

While automatic summarization techniques have made significant advancements, their primary focus has been on summarizing short news articles or documents that have clear structural patterns like scientific articles or government reports. There has not ...

Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress. Nonetheless, its capacity for out-of-distribution (OOD) generalization has been relatively underexplored. In this ...

Multi-task learning techniques have attracted great attention in recommendation systems because they can meet the needs of modeling multiple perspectives simultaneously and improve recommendation performance. As promising multi-task recommendation system ...

Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding of KGs, most of the current KG ...

Hypergraphs are effective in learning high-order relationships between nodes, which naturally represent group interactions as hyperedges (i.e., arbitrary-sized subsets of nodes). However, most approaches currently used for learning hypergraph ...

Since the development of artificial intelligence technology, E-Commerce has gradually become one of the world's largest commercial markets. Within this domain, sales events, which are based on sociological mechanisms, play a significant role. E-Commerce ...

With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine- tuning the parameters of an LLM specific to a downstream task with labeled ...

Sequential recommendation models generate embeddings for items through the analysis of historical user-item interactions and utilize the acquired embeddings to predict user preferences. Despite being effective in revealing personalized preferences for ...

As the field of machine reading comprehension (MRC) continues to evolve, it is unlocking enormous potential for its practical application. However, the currently well-performing models predominantly rely on massive pre-trained language models with at ...

Multilingual semantic retrieval involves retrieving semantically relevant content to a query irrespective of the language. Compared to monolingual and bilingual semantic retrieval, multilingual semantic retrieval requires a stronger alignment approach to ...

To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to ...