Search Results for author: Yongfeng Zhang

Found 77 papers, 35 papers with code

HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention

1 code implementation6 Mar 2023 Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, Yongfeng Zhang

The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding.

Exploration and Regularization of the Latent Action Space in Recommendation

no code implementations7 Feb 2023 Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang

To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.

Recommendation Systems

ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

no code implementations27 Jan 2023 Juntao Tan, Yongfeng Zhang

This paper presents ExplainableFold, an explainable AI framework for protein structure prediction.

Protein Structure Prediction

Transferable Fairness for Cold-Start Recommendation

no code implementations25 Jan 2023 Yunqi Li, Dingxian Wang, Hanxiong Chen, Yongfeng Zhang

The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.

Fairness Recommendation Systems

Causal Inference for Recommendation: Foundations, Methods and Applications

no code implementations8 Jan 2023 Shuyuan Xu, Jianchao Ji, Yunqi Li, Yingqiang Ge, Juntao Tan, Yongfeng Zhang

We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.

Causal Inference Fairness +1

Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing

no code implementations8 Nov 2022 Wenyue Hua, Lifeng Jin, Linfeng Song, Haitao Mi, Yongfeng Zhang, Dong Yu

Current natural language processing (NLP) models such as BERT and RoBERTa have achieved high overall performance, but they often make systematic errors due to bias or certain difficult features to learn.

Causal Structure Learning with Recommendation System

no code implementations19 Oct 2022 Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang

Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.

Decision Making Recommendation Systems

Dynamic Causal Collaborative Filtering

no code implementations23 Aug 2022 Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, Yongfeng Zhang

As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems.

Collaborative Filtering Recommendation Systems

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)

1 code implementation23 Aug 2022 Hanxiong Chen, Yunqi Li, He Zhu, Yongfeng Zhang

Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.

Neural Architecture Search

Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems

no code implementations17 Aug 2022 Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah

Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.

Explainable Recommendation Explanation Generation +3

GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations

no code implementations4 Aug 2022 Ziheng Chen, Fabrizio Silvestri, Jia Wang, Yongfeng Zhang, Zhenhua Huang, Hongshik Ahn, Gabriele Tolomei

Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.

Graph Classification Recommendation Systems

A Survey on Trustworthy Recommender Systems

no code implementations25 Jul 2022 Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.

Decision Making Explainable Recommendation +2

Fairness in Recommendation: A Survey

no code implementations26 May 2022 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang

It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.

Decision Making Fairness +1

AutoLossGen: Automatic Loss Function Generation for Recommender Systems

1 code implementation27 Apr 2022 Zelong Li, Jianchao Ji, Yingqiang Ge, Yongfeng Zhang

One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search.

Recommendation Systems

Explainable Fairness in Recommendation

no code implementations24 Apr 2022 Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, Yongfeng Zhang

In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.

Fairness Recommendation Systems

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

1 code implementation17 Feb 2022 Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, Yongfeng Zhang

For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.

Causal Inference

Personalized Prompt Learning for Explainable Recommendation

1 code implementation15 Feb 2022 Lei LI, Yongfeng Zhang, Li Chen

In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages.

Explainable Recommendation Recommendation Systems +1

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation

no code implementations14 Feb 2022 Xu Chen, Yongfeng Zhang, Ji-Rong Wen

Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them.

Explainable Recommendation Recommendation Systems

Neural Logic Analogy Learning

no code implementations4 Feb 2022 Yujia Fan, Yongfeng Zhang

The analogy learning problem then becomes a True/False evaluation problem of the logical expressions.

Logical Reasoning

RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems

1 code implementation12 Jan 2022 Zohreh Ovaisi, Shelby Heinecke, Jia Li, Yongfeng Zhang, Elena Zheleva, Caiming Xiong

Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.

Recommendation Systems

Graph Collaborative Reasoning

no code implementations27 Dec 2021 Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.

Link Prediction Logical Reasoning +2

From Kepler to Newton: Explainable AI for Science

no code implementations24 Nov 2021 Zelong Li, Jianchao Ji, Yongfeng Zhang

We show how computational and data-intensive methodology -- together with experimental and theoretical methodology -- can be seamlessly integrated for scientific research.

Deconfounded Causal Collaborative Filtering

no code implementations14 Oct 2021 Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, Yongfeng Zhang

Experiments on real-world e-commerce datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.

Collaborative Filtering Recommendation Systems

CLIP-Adapter: Better Vision-Language Models with Feature Adapters

1 code implementation9 Oct 2021 Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, Yu Qiao

Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.

Prompt Engineering Representation Learning

Counterfactual Evaluation for Explainable AI

no code implementations5 Sep 2021 Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, Yongfeng Zhang

While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.

Problem Learning: Towards the Free Will of Machines

no code implementations1 Sep 2021 Yongfeng Zhang

However, automatically discovering research or application problems for an area is beneficial since it helps to identify valid and potentially important problems hidden in data that are unknown to domain experts, expand the scope of tasks that we can do in an area, and even inspire completely new findings.

Counterfactual Explainable Recommendation

2 code implementations24 Aug 2021 Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang

Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.

Causal Inference Counterfactual Explanation +3

Personalized Transformer for Explainable Recommendation

1 code implementation ACL 2021 Lei LI, Yongfeng Zhang, Li Chen

Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words.

Explainable Recommendation Language Modelling +1

Personalized Counterfactual Fairness in Recommendation

1 code implementation20 May 2021 Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Yongfeng Zhang

Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.

Association Decision Making +2

Efficient Non-Sampling Knowledge Graph Embedding

1 code implementation21 Apr 2021 Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, Yongfeng Zhang

Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models.

Knowledge Graph Embedding

User-oriented Fairness in Recommendation

1 code implementation21 Apr 2021 Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang

To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.

Fairness Recommendation Systems +1

Faithfully Explainable Recommendation via Neural Logic Reasoning

1 code implementation NAACL 2021 Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang

Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.

Decision Making Explainable Recommendation +3

Variation Control and Evaluation for Generative SlateRecommendations

no code implementations26 Feb 2021 Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, Yongfeng Zhang

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations.

Recommendation Systems

EXTRA: Explanation Ranking Datasets for Explainable Recommendation

1 code implementation20 Feb 2021 Lei LI, Yongfeng Zhang, Li Chen

To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics.

Explainable Models Explainable Recommendation +1

Causal Collaborative Filtering

1 code implementation3 Feb 2021 Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang

In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation.

Collaborative Filtering Recommendation Systems

On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

2 code implementations1 Feb 2021 Lei LI, Yongfeng Zhang, Li Chen

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).

Learning-To-Rank Recommendation Systems

RomeBERT: Robust Training of Multi-Exit BERT

1 code implementation24 Jan 2021 Shijie Geng, Peng Gao, Zuohui Fu, Yongfeng Zhang

In this paper, we leverage gradient regularized self-distillation for RObust training of Multi-Exit BERT (RomeBERT), which can effectively solve the performance imbalance problem between early and late exits.

Natural Language Understanding

Discrete Knowledge Graph Embedding based on Discrete Optimization

no code implementations13 Jan 2021 Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.

Knowledge Graph Embedding

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Generate Natural Language Explanations for Recommendation

no code implementations9 Jan 2021 Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang

Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.

Denoising Explainable Recommendation +2

A Representation Learning Approach to Animal Biodiversity Conservation

no code implementations COLING 2020 Meet Mukadam, Mandhara Jayaram, Yongfeng Zhang

This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings.

Representation Learning

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

1 code implementation29 Oct 2020 Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang

User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.

Explainable Recommendation Knowledge Graphs +1

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce

no code implementations21 Aug 2020 Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo

In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.

Knowledge Graphs

Neural Logic Reasoning

3 code implementations20 Aug 2020 Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang

Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.

Logical Reasoning Recommendation Systems

E-commerce Recommendation with Weighted Expected Utility

no code implementations19 Aug 2020 Zhichao Xu, Yi Han, Yongfeng Zhang, Qingyao Ai

In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns.

Collaborative Filtering Recommendation Systems

Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation

no code implementations26 Jul 2020 Yikun Xian, Zuohui Fu, Qiaoying Huang, S. Muthukrishnan, Yongfeng Zhang

Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide explanations of why the recommended items were chosen.

Explainable Recommendation Knowledge Graphs +1

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers

no code implementations8 Jul 2020 Shijie Geng, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li, Anoop Cherian

Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content.

Answer Generation Graph Representation Learning

Understanding Echo Chambers in E-commerce Recommender Systems

1 code implementation6 Jul 2020 Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang

Current research on recommender systems mostly focuses on matching users with proper items based on user interests.

Recommendation Systems

Learning Personalized Risk Preferences for Recommendation

1 code implementation6 Jul 2020 Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang

Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.

Recommendation Systems

Learning Post-Hoc Causal Explanations for Recommendation

no code implementations30 Jun 2020 Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.

Association Sequential Recommendation

Neural Collaborative Reasoning

3 code implementations16 May 2020 Hanxiong Chen, Shaoyun Shi, Yunqi Li, Yongfeng Zhang

Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.

Collaborative Filtering Decision Making +3

HID: Hierarchical Multiscale Representation Learning for Information Diffusion

2 code implementations19 Apr 2020 Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia

In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.

Representation Learning

IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems

1 code implementation3 Feb 2020 Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen

We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

Representation Learning

Neural Logic Networks

no code implementations17 Oct 2019 Shaoyun Shi, Hanxiong Chen, Min Zhang, Yongfeng Zhang

The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of logical reasoning.

Collaborative Filtering Logical Reasoning

Explainable Product Search with a Dynamic Relation Embedding Model

no code implementations16 Sep 2019 Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft

Specifically, we propose to model the "search and purchase" behavior as a dynamic relation between users and items, and create a dynamic knowledge graph based on both the multi-relational product data and the context of the search session.


Conversational Product Search Based on Negative Feedback

no code implementations4 Sep 2019 Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft

So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.

Conversational Search

Attentive History Selection for Conversational Question Answering

2 code implementations26 Aug 2019 Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer

First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.

Conversational Question Answering Conversational Search +1

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

1 code implementation12 Jun 2019 Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang

To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.

Causal Inference Decision Making +3

BERT with History Answer Embedding for Conversational Question Answering

1 code implementation14 May 2019 Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, Mohit Iyyer

One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.

Conversational Question Answering Conversational Search +2

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

2 code implementations29 Apr 2019 Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose

In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.

Collaborative Filtering Recommendation Systems

Personalized Re-ranking for Recommendation

1 code implementation15 Apr 2019 Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.

Recommendation Systems Re-Ranking

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning +1

User Intent Prediction in Information-seeking Conversations

1 code implementation11 Jan 2019 Chen Qu, Liu Yang, Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, Minghui Qiu

Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.

Conversational Search Feature Engineering +1

Attentive Aspect Modeling for Review-aware Recommendation

no code implementations11 Nov 2018 Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua

The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product.

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

5 code implementations9 May 2018 Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang

Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items.

Collaborative Filtering Explainable Recommendation +3

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

1 code implementation1 May 2018 Liu Yang, Minghui Qiu, Chen Qu, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Jun Huang, Haiqing Chen

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Knowledge Distillation Retrieval +1

Explainable Recommendation: A Survey and New Perspectives

no code implementations30 Apr 2018 Yongfeng Zhang, Xu Chen

In this survey, we provide a comprehensive review for the explainable recommendation research.

Explainable Recommendation Product Recommendation +1

Analyzing and Characterizing User Intent in Information-seeking Conversations

no code implementations23 Apr 2018 Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.

Conversational Search Question Answering

Visually Explainable Recommendation

no code implementations31 Jan 2018 Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha

By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.

Explainable Recommendation Recommendation Systems

Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification

no code implementations11 Feb 2015 Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma

In this paper, we focus on the problem of phrase-level sentiment polarity labelling and attempt to bridge the gap between phrase-level and review-level sentiment analysis.

Classification General Classification +2

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