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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
Full-privacy secured search engine empowered by efficient...
Yuan-Yu Chang, Sheng-Tang Wong, Emmanuel O Salawu, Yu-Xuan Wang, · 2021-12-29 · via cs.IR updates on arXiv.org

Since the 90s, keyword-based search engines have been helping people locate relevant web content via a simple query, so have the recent full-text-based search engines mainly used for plagiarism detection following an article upload. However, these "free" or paid services operate by storing users' search queries and preferences for personal profiling and targeted ads delivery, while user-uploaded articles can further profit the service providers as part of their expanding databases. In short, search engine privacy has not been an option for web exploration in the past decades. Here we demonstrate that a database or internet search, provided with the entire article as a query, can be correctly carried out without revealing users' sensitive queries by an irreversible encoding scheme and an efficient FM-index search routine that is generally used in the NGS of genomes. In our solution, Sapiens Aperio Veritas Engine (S.A.V.E.), every word in the query is encoded into one of 12 "amino acids" (a.a.) comprising a pseudo-biological sequence (PBS) at users' local machines. The PBS-mediated plagiarism detection is done by users' submission of locally encoded PBS through our cloud service to locate identical duplicates in the collected web contents which had been encoded in the same way as the query. It is found that PBSs with a length longer than 12 a.a., can return correct results with a false positive rate <0.8%. S.A.V.E. runs at a similar speed as Bowtie and is 4 orders faster than BLAST. S.A.V.E., functioning in both regular and in-private search modes, provides a new option for efficient internet search and plagiarism detection in a compressed search space without a chance of storing and revealing users' confidential contents. We expect that future privacy-aware search engines can reference the ideas proposed herein. S.A.V.E. is made available at https://dyn.life.nthu.edu.tw/SAVE/