1.6k citations · 6.6k across the 108 of their papers we have counts for
35 papers · 1 filter
Alleviating the Long-Tail Problem in Conversational Recommender Systems
Zhipeng Zhao, Kun Zhou, Xiaolei Wang +4
Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are ve…
Reciprocal Sequential Recommendation
Bowen Zheng, Yupeng Hou, Wayne Xin Zhao +2
Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RR…
User Behavior Simulation with Large Language Model based Agents
Lei Wang, Jingsen Zhang, Hao Yang +11
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism…
TOME: A Two-stage Approach for Model-based Retrieval
Ruiyang Ren, Wayne Xin Zhao, Jing Liu +3
Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpo…
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou +3
In the past decades, recommender systems have attracted much attention in both research and industry communities, and a large number of studies have been devoted to developing effe…
Large Language Models are Zero-Shot Rankers for Recommender Systems
Yupeng Hou, Junjie Zhang, Zihan Lin +4
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.…
Multi-grained Hypergraph Interest Modeling for Conversational Recommendation
Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao +2
Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user's instant…
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Zhen Tian, Ting Bai, Wayne Xin Zhao +2
Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with…
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
Shanlei Mu, Penghui Wei, Wayne Xin Zhao +3
Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario.…
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
Zhen Tian, Ting Bai, Zibin Zhang +4
With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely incr…
Recent Advances in RecBole: Extensions with more Practical Considerations
Lanling Xu, Zhen Tian, Gaowei Zhang +10
RecBole has recently attracted increasing attention from the research community. As the increase of the number of users, we have received a number of suggestions and update request…
Dense Text Retrieval based on Pretrained Language Models: A Survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren +1
Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user's queries in natural language.…
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
Yupeng Hou, Zhankui He, Julian McAuley +1
Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to e…
Modeling Two-Way Selection Preference for Person-Job Fit
Chen Yang, Yupeng Hou, Yang Song +3
Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers.…
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Zihan Lin, Xuanhua Yang, Xiaoyu Peng +4
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). Whil…
RecBole 2.0: Towards a More Up-to-Date Recommendation Library
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan +16
In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics a…
Towards Universal Sequence Representation Learning for Recommender Systems
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao +3
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…
Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation
Zihan Lin, Hui Wang, Jingshu Mao +4
Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides…
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Shanlei Mu, Yupeng Hou, Wayne Xin Zhao +2
Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based o…
Negative Sampling for Contrastive Representation Learning: A Review
Lanling Xu, Jianxun Lian, Wayne Xin Zhao +5
The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great succe…
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
Xinyan Fan, Jianxun Lian, Wayne Xin Zhao +3
A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item…
CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space
Yupeng Hou, Binbin Hu, Zhiqiang Zhang +1
Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned…
Leveraging Search History for Improving Person-Job Fit
Yupeng Hou, Xingyu Pan, Wayne Xin Zhao +4
As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates. However, existin…
Filter-enhanced MLP is All You Need for Sequential Recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao +1
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteris…