Search Results for author: Yupeng Hou

Found 30 papers, 22 papers with code

Inductive Generative Recommendation via Retrieval-based Speculation

1 code implementation3 Oct 2024 Yijie Ding, Yupeng Hou, Jiacheng Li, Julian McAuley

In this paper, we propose SpecGR, a plug-and-play framework that enables GR models to recommend new items in an inductive setting.

Re-Ranking Retrieval

Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

1 code implementation19 Aug 2024 Chen Yang, Sunhao Dai, Yupeng Hou, Wayne Xin Zhao, Jun Xu, Yang song, HengShu Zhu

By utilizing the potential outcome framework, we further develop a model-agnostic causal reciprocal recommendation method that considers the causal effects of recommendations.

Recommendation Systems

Unlocking Decoding-time Controllability: Gradient-Free Multi-Objective Alignment with Contrastive Prompts

no code implementations9 Aug 2024 Tingchen Fu, Yupeng Hou, Julian McAuley, Rui Yan

The task of multi-objective alignment aims at balancing and controlling the different alignment objectives (e. g., helpfulness, harmlessness and honesty) of large language models to meet the personalized requirements of different users.

Multi-Behavior Generative Recommendation

1 code implementation27 May 2024 Zihan Liu, Yupeng Hou, Julian McAuley

We formulate the MBSR task into a consecutive two-step process: (1) given item sequences, MBGen first predicts the next behavior type to frame the user intention, (2) given item sequences and a target behavior type, MBGen then predicts the next items.

Sequential Recommendation

Mosaic-IT: Free Compositional Data Augmentation Improves Instruction Tuning

3 code implementations22 May 2024 Ming Li, Pei Chen, Chenguang Wang, Hongyu Zhao, Yijun Liang, Yupeng Hou, Fuxiao Liu, Tianyi Zhou

Finetuning large language models with a variety of instruction-response pairs has enhanced their capability to understand and follow instructions.

Data Augmentation Diversity +1

CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation

no code implementations11 Mar 2024 Junda Wu, Cheng-Chun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, Julian McAuley

Based on the retrieved user-item interactions, the LLM can analyze shared and distinct preferences among users, and summarize the patterns indicating which types of users would be attracted by certain items.

Recommendation Systems Reinforcement Learning (RL) +1

Bridging Language and Items for Retrieval and Recommendation

1 code implementation6 Mar 2024 Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, Julian McAuley

This paper introduces BLaIR, a series of pretrained sentence embedding models specialized for recommendation scenarios.

Retrieval Sentence +2

InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment

1 code implementation13 Feb 2024 Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu, Ming Gao, Julian McAuley

In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment.

Hallucination

Scaling Law of Large Sequential Recommendation Models

no code implementations19 Nov 2023 Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ji-Rong Wen

We find that scaling up the model size can greatly boost the performance on these challenging tasks, which again verifies the benefits of large recommendation models.

Sequential Recommendation

Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation

1 code implementation15 Nov 2023 Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, Ming Chen, Ji-Rong Wen

To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems.

Quantization Recommendation Systems

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

no code implementations13 Oct 2023 Junjie Zhang, Yupeng Hou, Ruobing Xie, Wenqi Sun, Julian McAuley, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen

The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea.

Collaborative Filtering Decision Making +2

Reciprocal Sequential Recommendation

1 code implementation26 Jun 2023 Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang song, HengShu Zhu

Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties.

Sequential Recommendation

Large Language Models are Zero-Shot Rankers for Recommender Systems

2 code implementations15 May 2023 Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, Wayne Xin Zhao

Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.

Recommendation Systems

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

no code implementations11 May 2023 Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen

Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.

Instruction Following Language Modelling +2

Multi-grained Hypergraph Interest Modeling for Conversational Recommendation

1 code implementation4 May 2023 Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Jing Zhang

In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.

Conversational Recommendation Recommendation Systems

A Survey of Large Language Models

5 code implementations31 Mar 2023 Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, YiFan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen

To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.

Language Modelling Survey

Recent Advances in RecBole: Extensions with more Practical Considerations

1 code implementation28 Nov 2022 Lanling Xu, Zhen Tian, Gaowei Zhang, Lei Wang, Junjie Zhang, Bowen Zheng, YiFan Li, Yupeng Hou, Xingyu Pan, Yushuo Chen, Wayne Xin Zhao, Xu Chen, Ji-Rong Wen

In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.

Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

1 code implementation22 Oct 2022 Yupeng Hou, Zhankui He, Julian McAuley, Wayne Xin Zhao

Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives.

Language Modelling Recommendation Systems +1

Privacy-Preserved Neural Graph Similarity Learning

1 code implementation21 Oct 2022 Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.

Graph Matching Graph Similarity +1

Modeling Two-Way Selection Preference for Person-Job Fit

1 code implementation18 Aug 2022 Chen Yang, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen, Wayne Xin Zhao

To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph.

Contrastive Learning Graph Representation Learning +1

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

2 code implementations15 Jun 2022 Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen

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 and architectures.

Benchmarking Data Augmentation +3

Towards Universal Sequence Representation Learning for Recommender Systems

2 code implementations13 Jun 2022 Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.

Recommendation Systems Representation Learning

ID-Agnostic User Behavior Pre-training for Sequential Recommendation

no code implementations6 Jun 2022 Shanlei Mu, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Bolin Ding

Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information.

Attribute Language Modelling +1

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

1 code implementation23 Apr 2022 Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session.

Session-Based Recommendations

Leveraging Search History for Improving Person-Job Fit

no code implementations27 Mar 2022 Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Shuqing Bian, Yang song, Tao Zhang, Ji-Rong Wen

As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.

Text Matching

Neural Graph Matching for Pre-training Graph Neural Networks

1 code implementation3 Mar 2022 Yupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, Ji-Rong Wen

In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.

Graph Matching

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

1 code implementation13 Feb 2022 Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao

For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.

Collaborative Filtering Contrastive Learning

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

1 code implementation3 Nov 2020 Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen

In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.

Collaborative Filtering Sequential Recommendation

Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

no code implementations25 Sep 2020 Shuqing Bian, Xu Chen, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen

Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.

Text Matching

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