Daily edition · Information Retrieval
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31 articles · 5 sources · 30 papers ·
Top topics: Information Retrieval · AI Research · AI
arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and…
arXiv:2609.17564v1 Announce Type: new Abstract: Deploying retrieval-augmented generation (RAG) on commodity GPUs such as the NVIDIA T4 (16 GB VRAM) exposes a practical failure mode we call the Compression Paradox:…
arXiv:2608.11846v2 Announce Type: replace Abstract: Capturing user preference from a user's interaction sequence is the central challenge of Sequential Recommendation (SR). This preference intuitively emerges from…
arXiv:2510.04816v1 Announce Type: cross Abstract: Accurately predicting conversion rate (CVR) is essential in various recommendation domains such as online advertising systems and e-commerce. These systems utilize user…
arXiv:2405.02228v5 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustworthy scientific…
arXiv:2609.18437v1 Announce Type: new Abstract: In this work, we explore whether LLMs can accurately predict and explain plausible materials for vehicle components such as brake discs or fuel injectors without requiring…
arXiv:2607.24804v3 Announce Type: replace Abstract: Recommendation systems have undergone significant transformations in the past years. The transition from traditional feature interaction modules to generative…
arXiv:2609.18459v1 Announce Type: cross Abstract: Encrypted communication protects sensitive user data but can facilitate harmful or unlawful exchanges, creating a trade-off between detecting dangerous messages and…
arXiv:2609.17764v1 Announce Type: cross Abstract: Attention-based rerankers score documents by aggregating query-to-document attention and subtracting a null-query calibration pass to remove positional and structural…
arXiv:2609.18695v1 Announce Type: new Abstract: The increasing demand for user privacy and compliance with regulations such as GDPR has made machine unlearning a fundamental requirement for modern recommender systems.…
arXiv:2609.13993v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit strong semantic understanding and preference reasoning capabilities, offering new opportunities for user modeling in recommender…
arXiv:2609.18042v1 Announce Type: new Abstract: Voice assistants grounded in external knowledge typically use automatic speech recognition (ASR) to transcribe speech queries before retrieving evidence from textual…
arXiv:2510.24870v3 Announce Type: replace-cross Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a more prevalent…
arXiv:2408.07702v2 Announce Type: cross Abstract: Schema linking is a crucial step in Text-to-SQL pipelines. Its goal is to retrieve the relevant tables and columns of a target database for a user's query while…
arXiv:2609.17709v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query…
arXiv:2602.12354v3 Announce Type: replace Abstract: LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender…
arXiv:2609.18341v1 Announce Type: cross Abstract: When someone asks an AI assistant which doctor to see or which firm to trust with their savings, the answer is a referral. We audit AI provider recommendations in four…
arXiv:2609.17639v1 Announce Type: new Abstract: Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these signals reveal what users…
arXiv:2609.18296v1 Announce Type: new Abstract: Traditional retrieval systems typically use multi-stage cascading architectures (MCA), where each module is optimized independently, leading to inconsistent objectives and…
arXiv:2609.18188v1 Announce Type: new Abstract: AI-assisted sourcing streamlines candidate review, reducing the administrative burden of manual screening for recruiters. However, deploying language models as production…
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