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cs.IR updates on arXiv.org

From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations JFinTEB: Japanese Financial Text Embedding Benchmark UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts Collaborative Filtering Through Weighted Similarities of User and Item Embeddings IG-Search: Step-Level Information Gain Rewards for Search-Augmented Reasoning Metric-agnostic Learning-to-Rank via Boosting and Rank Approximation GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model Evaluation of Agents under Simulated AI Marketplace Dynamics
AllSERP: Exhaustive Per-Element Enrichment of the Versati...
[Submitted on 6 May 2026 (v1), last revised 23 Jul 2026 (this ve · 2026-05-06 · via cs.IR updates on arXiv.org

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Abstract:We release AllSERP, a typed AOI and per-element behavioral enrichment of the AdSERP commercial-intent SERP corpus [4]. AdSERP ships 2,776 trials of full-page screenshots, captured SERP HTML, 150 Hz Gazepoint eye tracking, evtrack mouse telemetry, scroll, and pupil signals against real Google SERPs collected before AI Overviews -- but its bounding boxes cover only ad surfaces (15.5 % of attributable clicks). AllSERP adds pixel-accurate organic and widget bboxes via screenshot-anchored CV, semantic types across thirteen element types via an HTML parser, an inter-result gap-fill flavor (typed_gapfill), and X+Y click attribution that reaches 91.7 % of the corpus while flagging the rest at trial level. The Phase C ad-vs-non-ad partition is internally consistent with the shipped ad rectangles (0 disagreements across 38,250 classifications). We ship the pipeline, per-trial JSONs, a corpus CSV, and a browser-based replay viewer; everything is reproducible from the AdSERP Zenodo volume. The release enables per-element click, fixation, regression, and above-fold analyses that the shipped ads-vs-organic split could not resolve.

Submission history

From: K. Andrew Edmonds [view email]
[v1] Wed, 6 May 2026 14:14:35 UTC (4,504 KB)
[v2] Tue, 19 May 2026 03:18:22 UTC (4,516 KB)
[v3] Thu, 23 Jul 2026 15:14:51 UTC (4,613 KB)