Daily edition · Information Retrieval
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49 articles · 2 sources · 49 papers ·
Top topics: AI Research · AI · Information Retrieval
arXiv:2609.01617v1 Announce Type: cross Abstract: Getting accurate, grounded answers out of large enterprise document repositories is a difficult problem. Dense vector retrieval alone frequently performs poorly on…
arXiv:2609.01618v1 Announce Type: cross Abstract: Telecom Service and Network Operations Centers (SNOCs) rely on large collections of cloud documents, including Standard Operating Procedures (SOPs), vendor technical…
arXiv:2609.01628v1 Announce Type: cross Abstract: E-commerce search ranking must balance multiple objectives--relevance, user engagement, and platform revenue--when allocating impression slots to competing listings.…
arXiv:2609.00570v2 Announce Type: replace-cross Abstract: With the growing scale of multi-agent architectures and large language models, deployed AI assistants are increasingly tasked with reasoning over long,…
arXiv:2609.02499v1 Announce Type: cross Abstract: Recommender-system experiments often rely on a single random training seed, assuming that run-to-run stochasticity has limited impact on evaluation conclusions. This…
arXiv:2606.29894v2 Announce Type: replace-cross Abstract: As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theorem…
arXiv:2609.01612v1 Announce Type: new Abstract: We introduce MESSY STREETS, a benchmark for evaluating geocoders on verbatim web addresses, with existence verification and controlled measurement of surface-form…
arXiv:2609.01780v1 Announce Type: new Abstract: Knowledge Graph Question Answering (KGQA) over RDF graphs remains challenging in domain-specific settings, where formal ontologies and curated text-SPARQL pairs are often…
arXiv:2609.02316v1 Announce Type: new Abstract: Generative engine optimization (GEO) enables content producers to increase the visibility of their web pages in generative search engines, but the same techniques can…
arXiv:2609.01807v1 Announce Type: cross Abstract: Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive decoding spends one…
arXiv:2604.20845v2 Announce Type: replace Abstract: Next Point-of-Interest (POI) recommendation ranks a user's likely next location based on check-in history. Most recent rankers compress the trajectory into a single…
arXiv:2609.01913v1 Announce Type: new Abstract: In product entity resolution, relationship definitions constantly evolve with business needs, yet adapting to each change traditionally requires slow, costly human…
arXiv:2609.01654v1 Announce Type: new Abstract: Existing text-video retrieval datasets primarily consist of short-form clips containing a single dominant event. While suitable for measuring basic vision-language…
arXiv:2609.01636v1 Announce Type: new Abstract: From-Scratch Name Disambiguation (SND) groups papers sharing an ambiguous name into clusters of distinct real-world authors. Existing methods suffer from two critical…
arXiv:2609.02011v1 Announce Type: cross Abstract: Large language model question answering over power-grid models must respect a fixed context budget. We introduce seed-anchored graph rendering, a deterministic method…
arXiv:2609.01622v1 Announce Type: new Abstract: The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recommender models. This…
arXiv:2609.02303v1 Announce Type: new Abstract: Publishing research data is widely expected to increase its reuse and to inspire new research. In the social sciences, data from surveys, interviews, polls, and statistics…
arXiv:2609.02162v1 Announce Type: new Abstract: Serving useful recommendations under distribution shift is crucial for balancing utility and risk in out-of-distribution (OOD) recommendation. However, most existing OOD…
arXiv:2609.02671v1 Announce Type: new Abstract: Sequential recommendation models are foundational to modern personalized services, yet their effectiveness varies substantially across heterogeneous user environments. In…
arXiv:2609.01619v1 Announce Type: new Abstract: We propose a novel Multi-Interest Sequence Recommendation Framework with \underline{M}asking \underline{G}NN-Guided \underline{Diff}usion Model (MGDiff), designed to…
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