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Maximizing Marginal Fairness for Dynamic Learning to Rank
Rankings, especially those in search and recommendation systems, often d...
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A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
User and item reviews are valuable for the construction of recommender s...
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Controlling the Risk of Conversational Search via Reinforcement Learning
Users often formulate their search queries with immature language withou...
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A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems
Using reviews to learn user and item representations is important for re...
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Review Regularized Neural Collaborative Filtering
In recent years, text-aware collaborative filtering methods have been pr...
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Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Leveraging biased click data for optimizing learning to rank systems has...
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E-commerce Recommendation with Weighted Expected Utility
Different from shopping at retail stores, consumers on e-commerce platfo...
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A Transformer-based Embedding Model for Personalized Product Search
Product search is an important way for people to browse and purchase ite...
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Unbiased Learning to Rank: Online or Offline?
How to obtain an unbiased ranking model by learning to rank with biased ...
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A Review-based Transformer Model for Personalized Product Search
In product search, customers make purchase decisions based on not only t...
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SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
In learning-to-rank for information retrieval, a ranking model is automa...
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Explainable Product Search with a Dynamic Relation Embedding Model
Product search is one of the most popular methods for customers to disco...
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Conversational Product Search Based on Negative Feedback
Intelligent assistants change the way people interact with computers and...
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A Zero Attention Model for Personalized Product Search
Product search is one of the most popular methods for people to discover...
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A Deep Look into Neural Ranking Models for Information Retrieval
Ranking models lie at the heart of research on information retrieval (IR...
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Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match
Relevance feedback techniques assume that users provide relevance judgme...
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Revisiting Iterative Relevance Feedback for Document and Passage Retrieval
As more and more search traffic comes from mobile phones, intelligent as...
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Learning Groupwise Scoring Functions Using Deep Neural Networks
While in a classification or a regression setting a label or a value is ...
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Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
Providing model-generated explanations in recommender systems is importa...
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Unbiased Learning to Rank with Unbiased Propensity Estimation
Learning to rank with biased click data is a well-known challenge. A var...
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Learning a Deep Listwise Context Model for Ranking Refinement
Learning to rank has been intensively studied and widely applied in info...
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Learning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborati...
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aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
As an alternative to question answering methods based on feature enginee...
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A Deep Relevance Matching Model for Ad-hoc Retrieval
In recent years, deep neural networks have led to exciting breakthroughs...
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Adaptability of Neural Networks on Varying Granularity IR Tasks
Recent work in Information Retrieval (IR) using Deep Learning models has...
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