Search Results for author: Weiwen Liu

Found 24 papers, 12 papers with code

Multi-domain Recommendation with Embedding Disentangling and Domain Alignment

1 code implementation10 Aug 2023 Wentao Ning, Xiao Yan, Weiwen Liu, Reynold Cheng, Rui Zhang, Bo Tang

We propose a new MDR method named EDDA with two key components, i. e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively.

Transfer Learning

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

1 code implementation19 Jun 2023 Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.

Recommendation Systems

How Can Recommender Systems Benefit from Large Language Models: A Survey

1 code implementation9 Jun 2023 Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang

For the "WHERE" question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i. e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller.

Ethics Feature Engineering +4

Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation

no code implementations7 Jun 2023 Xianyu Chen, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, Menghui Zhu, Ruiming Tang, Kai Dong, Dingyin Xia, Yong Yu

Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.

Knowledge Tracing Recommendation Systems

A Survey on User Behavior Modeling in Recommender Systems

no code implementations22 Feb 2023 ZhiCheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, Ruiming Tang

Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.

Recommendation Systems

A Bird's-eye View of Reranking: from List Level to Page Level

1 code implementation17 Nov 2022 Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu

Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.

Recommendation Systems

An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

1 code implementation17 Jun 2022 Lingyue Fu, Jianghao Lin, Weiwen Liu, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i. e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately.

Information Retrieval Retrieval

Multi-Level Interaction Reranking with User Behavior History

1 code implementation20 Apr 2022 Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.

Recommendation Systems

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

no code implementations23 Mar 2022 Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He

Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.

Recommendation Systems Re-Ranking

Neural Re-ranking in Multi-stage Recommender Systems: A Review

1 code implementation14 Feb 2022 Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.

Recommendation Systems Re-Ranking

Context-aware Reranking with Utility Maximization for Recommendation

no code implementations18 Oct 2021 Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu

As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.

Graph Attention Recommendation Systems

Retrieval & Interaction Machine for Tabular Data Prediction

1 code implementation11 Aug 2021 Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.

Click-Through Rate Prediction Recommendation Systems +1

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

no code implementations25 Jun 2021 Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, Pheng Ann Heng

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.

Fairness Recommendation Systems +2

An Adversarial Imitation Click Model for Information Retrieval

1 code implementation13 Apr 2021 Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.

Imitation Learning Information Retrieval +2

Opportunistic Multi-aspect Fairness through Personalized Re-ranking

no code implementations21 May 2020 Nasim Sonboli, Farzad Eskandanian, Robin Burke, Weiwen Liu, Bamshad Mobasher

In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results.

Fairness Recommendation Systems +1

Inter-sequence Enhanced Framework for Personalized Sequential Recommendation

no code implementations25 Apr 2020 Feng Liu, Weiwen Liu, Xutao Li, Yunming Ye

Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users' preferences over candidate items.

Sequential Recommendation

Consistency-Aware Recommendation for User-Generated ItemList Continuation

1 code implementation30 Dec 2019 Yun He, Yin Zhang, Weiwen Liu, James Caverlee

Complementary to methods that exploit specific content patterns (e. g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e. g., videos, images, books).

Contextual Combinatorial Conservative Bandits

no code implementations26 Nov 2019 Xiaojin Zhang, Shuai Li, Weiwen Liu, Shengyu Zhang

The problem of multi-armed bandits (MAB) asks to make sequential decisions while balancing between exploitation and exploration, and have been successfully applied to a wide range of practical scenarios.

Multi-Armed Bandits

Wasserstein Collaborative Filtering for Item Cold-start Recommendation

no code implementations10 Sep 2019 Yitong Meng, Guangyong Chen, Benben Liao, Jun Guo, Weiwen Liu

We further adopt the idea of CF and propose Wasserstein CF (WCF) to improve the recommendation performance on cold-start items.

Collaborative Filtering

PMD: An Optimal Transportation-based User Distance for Recommender Systems

no code implementations10 Sep 2019 Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Benben Liao, Jun Guo, Guangyong Chen

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users.

Collaborative Filtering Recommendation Systems

Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization

no code implementations13 Jun 2019 Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang

The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work.

Drug Discovery Quantum Chemistry Regression

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