Maarten de Rijke
16 papers in the PaperMetrix corpus
Papers by this author
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Click Models for Web Search and their Applications to IR
2016
In this tutorial we give an overview of click models for web search. We show how the framework of probabilistic graphical models helps to explain user behavior, build new evaluation metrics and perform simulations. The …
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RefNet: A Reference-aware Network for Background Based Conversation
2019 · arXiv (Cornell University)
Existing conversational systems tend to generate generic responses. Recently, Background Based Conversations (BBCs) have been introduced to address this issue. Here, the generated responses are grounded in some background information. The proposed methods for BBCs …
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Attribute-aware Diversification for Sequential Recommendations
2020 · UvA-DARE (University of Amsterdam)
Users prefer diverse recommendations over homogeneous ones. However, most previous work on Sequential Recommenders does not consider diversity, and strives for maximum accuracy, resulting in homogeneous recommendations. In this paper, we consider both accuracy and …
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Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
2020 · arXiv (Cornell University)
Existing methods for Dialogue Response Generation (DRG) in Task-oriented Dialogue Systems (TDSs) can be grouped into two categories: template-based and corpus-based. The former prepare a collection of response templates in advance and fill the slots …
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Standing in Your Shoes: External Assessments for Personalized Recommender Systems
2021
The evaluation of recommender systems relies on user preference data, which is difficult to acquire directly because of its subjective nature. Current recommender systems widely utilize users' historical interactions as implicit or explicit feedback, but …
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News Article Retrieval in Context for Event-centric Narrative Creation
2021
Writers such as journalists often use automatic tools to find relevant content to include in their narratives. In this paper, we focus on supporting writers in the news domain to develop event-centric narratives. Given an …
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Contrastive Learning Reduces Hallucination in Conversations
2022 · arXiv (Cornell University)
Pre-trained language models (LMs) store knowledge in their parameters and can generate informative responses when used in conversational systems. However, LMs suffer from the problem of "hallucination:" they may generate plausible-looking statements that are irrelevant …
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Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
2024 · Proceedings of the AAAI Conference on Artificial Intelligence
Neural ranking models (NRMs) have shown great success in information retrieval (IR). But their predictions can easily be manipulated using adversarial examples, which are crafted by adding imperceptible perturbations to legitimate documents. This vulnerability raises …
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Generative Retrieval as Multi-Vector Dense Retrieval
2024 · arXiv (Cornell University)
Generative retrieval generates identifiers of relevant documents in an end-to-end manner using a sequence-to-sequence architecture for a given query. The relation between generative retrieval and other retrieval methods, especially those based on matching within dense …
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Cognitive Biases in Large Language Models for News Recommendation
2024 · arXiv (Cornell University)
Despite large language models (LLMs) increasingly becoming important components of news recommender systems, employing LLMs in such systems introduces new risks, such as the influence of cognitive biases in LLMs. Cognitive biases refer to systematic …
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Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers
2025
Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques. However, effectively enabling LLMs to seek accurate knowledge in complex tasks remains a challenge due to the complexity of multi-hop …
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Short Text Similarity with Word Embeddings
2015
Determining semantic similarity between texts is important in many tasks in information retrieval such as search, query suggestion, automatic summarization and image finding. Many approaches have been suggested, based on lexical matching, handcrafted patterns, syntactic …
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A Collaborative Session-based Recommendation Approach with Parallel Memory Modules
2019
Session-based recommendation is the task of predicting the next item to recommend when the only available information consists of anonymous behavior sequences. Previous methods for session-based recommendation focus mostly on the current session, ignoring collaborative …
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RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation
2019 · Proceedings of the AAAI Conference on Artificial Intelligence
Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), …
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Query Resolution for Conversational Search with Limited Supervision
2020
In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is …
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Mixed Information Flow for Cross-Domain Sequential Recommendations
2022 · ACM Transactions on Knowledge Discovery from Data
Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain …