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Found 77 papers, 35 papers with code
Improving Personalized Explanation Generation through Visualization
In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items.
HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention
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
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.
ExplainableFold: Understanding AlphaFold Prediction with Explainable AI
This paper presents ExplainableFold, an explainable AI framework for protein structure prediction.
Transferable Fairness for Cold-Start Recommendation
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.
Causal Inference for Recommendation: Foundations, Methods and Applications
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.
Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing
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
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.
Dynamic Causal Collaborative Filtering
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.
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.
Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems
Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
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.
A Survey on Trustworthy Recommender Systems
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.
Fairness in Recommendation: A Survey
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.
AutoLossGen: Automatic Loss Function Generation for Recommender Systems
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.
Explainable Fairness in Recommendation
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.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives.
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
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.
Personalized Prompt Learning for Explainable Recommendation
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.
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation
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.
Neural Logic Analogy Learning
The analogy learning problem then becomes a True/False evaluation problem of the logical expressions.
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness.
Graph Collaborative Reasoning
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
From Kepler to Newton: Explainable AI for Science
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
Experiments on real-world e-commerce datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Large-scale contrastive vision-language pre-training has shown significant progress in visual representation learning.
Counterfactual Evaluation for Explainable AI
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
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
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.
Personalized Transformer for Explainable Recommendation
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.
Personalized Counterfactual Fairness in Recommendation
Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.
Efficient Non-Sampling Knowledge Graph Embedding
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.
User-oriented Fairness in Recommendation
To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.
Faithfully Explainable Recommendation via Neural Logic Reasoning
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.
Variation Control and Evaluation for Generative SlateRecommendations
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.
EXTRA: Explanation Ranking Datasets for Explainable Recommendation
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.
Causal Collaborative Filtering
In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation.
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
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).
RomeBERT: Robust Training of Multi-Exit BERT
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.
Discrete Knowledge Graph Embedding based on Discrete Optimization
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.
Towards Long-term Fairness in Recommendation
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.
Generate Natural Language Explanations for Recommendation
Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.
A Representation Learning Approach to Animal Biodiversity Conservation
This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings.
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
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.
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.
Neural Logic Reasoning
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.
E-commerce Recommendation with Weighted Expected Utility
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.
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
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.
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers
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.
Understanding Echo Chambers in E-commerce Recommender Systems
Current research on recommender systems mostly focuses on matching users with proper items based on user interests.
Learning Personalized Risk Preferences for Recommendation
Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.
Learning Post-Hoc Causal Explanations for Recommendation
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.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
Neural Collaborative Reasoning
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.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
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.
Neural Logic Networks
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.
Explainable Product Search with a Dynamic Relation Embedding Model
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
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.
Attentive History Selection for Conversational Question Answering
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
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.
BERT with History Answer Embedding for Conversational Question Answering
One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.
Personalized Re-ranking for Recommendation
Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems
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.
Answer Interaction in Non-factoid Question Answering Systems
Information retrieval systems are evolving from document retrieval to answer retrieval.
User Intent Prediction in Information-seeking Conversations
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.
Attentive Aspect Modeling for Review-aware Recommendation
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
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.
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Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
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.
Explainable Recommendation: A Survey and New Perspectives
In this survey, we provide a comprehensive review for the explainable recommendation research.
Analyzing and Characterizing User Intent in Information-seeking Conversations
Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems.
Learning over Knowledge-Base Embeddings for Recommendation
In this work, we propose to reason over knowledge base embeddings for personalized recommendation.
Visually Explainable Recommendation
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.
Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation
We further evaluate the neural matching models in the next question prediction task in conversations.
Boost Phrase-level Polarity Labelling with Review-level Sentiment Classification
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.
