Wayne Xin Zhao

Dual Sparse Attention Network For Session-based Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Session-based Recommendations recommend the next possible item for the user with anonymous sessions, whose challenge is that the user’s behavioral preference can only be analyzed in a limited sequence to meet their need. Recent advances …

Counterfactual Review-based Recommendation

2021

Incorporating review information into the recommender system has been demonstrated to be an effective method for boosting the recommendation performance. Previous research mainly focus on designing advanced architectures to better profile the users and items. …

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

2023 · arXiv (Cornell University)

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark …

TextBox 2.0: A Text Generation Library with Pre-trained Language Models

2022

Tianyi Tang, Junyi Li, Zhipeng Chen, Yiwen Hu, Zhuohao Yu, Wenxun Dai, Wayne Xin Zhao, Jian-yun Nie, Ji-rong Wen. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2022.

Enhancing Sequential Recommender with Large Language Models for Joint Video and Comment Recommendation

2024 · arXiv (Cornell University)

Nowadays, reading or writing comments on captivating videos has emerged as a critical part of the viewing experience on online video platforms. However, existing recommender systems primarily focus on users' interaction behaviors with videos, neglecting …

YuLan: An Open-source Large Language Model

2024 · arXiv (Cornell University)

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training …

Towards Effective Code-Integrated Reasoning

2025 · arXiv (Cornell University)

In this paper, we investigate code-integrated reasoning, where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire this capability, models must learn when and how to use …

DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation

2025 · arXiv (Cornell University)

Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation backbones. However, existing LLM-based RSs often do not fully exploit the complementary …

A Neural Collaborative Filtering Model with Interaction-based Neighborhood

2017

Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, …

Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks

2018

With the revival of neural networks, many studies try to adapt powerful sequential neural models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although …

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network

2018

Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model

2018

Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have …

Local and Global Information Fusion for Top-N Recommendation in Heterogeneous Information Network

2018

Since heterogeneous information network (HIN) is able to integrate complex information and contain rich semantics, there is a surge of HIN based recommendation in recent years. Although existing methods have achieved performance improvement to some …

Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation

2019

In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling …

Heterogeneous Information Network Embedding for Recommendation

2018 · IEEE Transactions on Knowledge and Data Engineering

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, calledHIN based recommendation. It is challenging to develop effective methods …

S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

2020

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained …

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

2020

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself …

Towards Topic-Guided Conversational Recommender System

2020

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from …

Counterfactual Data-Augmented Sequential Recommendation

2021

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose …

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

2021

Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: …

A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

2021

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the …

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to …

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

2021

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation …

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

2022 · Proceedings of the ACM Web Conference 2022

Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs. Despite the effectiveness, these methods suffer from data sparsity …

Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation …

Towards Universal Sequence Representation Learning for Recommender Systems

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developing the sequence …

A Survey of Large Language Models

2026 · Frontiers of Computer Science

Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, …