Results 21 to 30 of about 1,749 (37)
Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
, 2016 The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists.
Dufour, Richard +4 more
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Graph Neural Re-Ranking via Corpus Graph
Re-ranking systems aim to reorder an initial list of documents to satisfy better the information needs associated with a user-provided query. Modern re-rankers predominantly rely on neural network models, which have proven highly effective in ...
Di Francesco, Andrea Giuseppe +3 more
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Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data.
de Rijke, Maarten +4 more
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"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine-tuning the parameters of an LLM specific to a downstream task with labeled ...
Chandra, Manish +2 more
core +1 more source
Generative Retrieval via Term Set Generation
Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a generative model to directly generate the relevant DocID for the input query.
Cao, Zhao +5 more
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Weighted KL-Divergence for Document Ranking Model Refinement
Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning.
He, Shanxiu +3 more
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DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate ...
Ai, Qingyao +7 more
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The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to search
Ding, Xichen +6 more
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In news recommendation systems, reducing popularity bias is essential for delivering accurate and diverse recommendations. This paper presents POPK, a new method that uses temporal-counterfactual analysis to mitigate the influence of popular news ...
Azevedo, Igor L. R. +2 more
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