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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 Driving Engagement in Daily Fantasy Sports with a Scalable and Urgency-Aware Ranking Engine TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds Hybrid Retrieval for COVID-19 Literature: Comparing Rank Fusion and Projection Fusion with Diversity Reranking FRAGATA: Semantic Retrieval of HPC Support Tickets via Hybrid RAG over 20 Years of Request Tracker History Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines Indexing Multimodal Language Models for Large-scale Image Retrieval FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents Knowledge Graph RAG: Agentic Crawling and Graph Construction in Enterprise Documents NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books Frugal Knowledge Graph Construction with Local LLMs: A 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Benchmarking RAG and GraphRAG for Agentic Search Systems Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model SocialWise: LLM-Agentic Conversation Therapy for Individuals with Autism Spectrum Disorder to Enhance Communication Skills Working Notes on Late Interaction Dynamics: Analyzing Targeted Behaviors of Late Interaction Models Resolving the Robustness-Precision Trade-off in Financial RAG through Hybrid Document-Routed Retrieval Spectral Tempering for Embedding Compression in Dense Passage Retrieval AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates A Domain-Specific Language for LLM-Driven Trigger Generation in Multimodal Data Collection MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics Exploring Structural Complexity in Normative RAG with Graph-based approaches: A case study on the ETSI Standards SRBench: A Comprehensive Benchmark for Sequential Recommendation with Large Language Models MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults RLPO: Residual Listwise Preference Optimization for Long-Context Review Ranking When & How to Write for Personalized Demand-aware Query Rewriting in Video Search Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows WisPaper: Your AI Scholar Search Engine GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs Hierarchical Semantic Retrieval with Cobweb WARBERT: A Hierarchical BERT-based Model for Web API Recommendation Reliable Evaluation Protocol for Low-Precision Retrieval VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
R-Rec: A rule-based system for contextual suggestion using tag-description similarity
Kshitij Singh, Manajit Chakraborty, C. Ravindranath Chowdary · 2017-07-05 · via cs.IR updates on arXiv.org

Contextual Suggestion deals with search techniques for complex information needs that are highly focused on context and user needs. In this paper, we propose \emph{R-Rec}, a novel rule-based technique to identify and recommend appropriate points-of-interest to a user given her past preferences. We try to embody the information that the user shares in the form of rating and tags of any previous point(s)-of-interest and use it to rank the unrated candidate suggestions. The ranking function is computed based on the similarity between a suggestion and the places that the user like and the dissimilarity between the suggestion and the places disliked by the user. Experiments carried out on TREC-Contextual Suggestion 2015 dataset reveal the efficacy of our method.

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