Topic - Edition
Automatic language redirects seem helpful. In practice they cause some of the worst multilingual bugs: users stuck in the wrong language, search engines unable to crawl alternate versions, and shared links that open…
arXiv:2610.36544v1 Announce Type: cross Abstract: Classification accuracy and true-positive and false-positive rates alone do not determine whether restarting an AI agent improves success. Using an exact alarm-to-action…
arXiv:2610.37570v1 Announce Type: cross Abstract: We present LCM-Promo, an LLM-based uplift model for promotion allocation. LCM-Promo is a task-specific Large Commerce Model (LCM): a finetuned LLM trained on…
arXiv:2610.37286v1 Announce Type: new Abstract: Semantic ID-based recommendation represents each item as a sequence of semantic tokens obtained through residual quantization. This paradigm generates the next item's…
arXiv:2610.36383v1 Announce Type: cross Abstract: For efficiency, industrial large language model (LLM) labelers often pack many candidate labels into one prompt and generate a YES/NO answer for each, even when each…
arXiv:2610.37280v1 Announce Type: new Abstract: Narrative-driven recommendation allows users to express multifaceted needs through free-form text where stated preferences naturally qualify and condition one another.…
arXiv:2610.35542v2 Announce Type: replace Abstract: Long-context multimodal document question answering (QA) has become an important yet challenging task for modern vision-language models (VLMs), as it requires…
arXiv:2605.27810v2 Announce Type: replace Abstract: Large language models (LLMs) offer strong semantic representations for ranking, but processing candidate texts within limited contexts and repeatedly invoking an LLM…
arXiv:2610.36208v1 Announce Type: new Abstract: This reproducibility paper explores the counterfactual explanation technique known as Learning to eXplain Recommendations (LXR). Prior work has shown that LXR can reliably…
arXiv:2610.37358v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) can benefit from more sophisticated retrieval, but extra retrieval may be unnecessary for many questions and gains in evidence…
arXiv:2610.37267v1 Announce Type: cross Abstract: Text-based Person Anomaly Search (TPAS) retrieves a target pedestrian using natural-language descriptions of appearance and normal or anomalous behavior. Unlike…
arXiv:2610.36408v1 Announce Type: new Abstract: Daily item co-occurrence embeddings are paired with next-day behavior without future-behavior leakage. Multi-day click-through rate (CTR) training therefore mixes versions…
arXiv:2610.37584v1 Announce Type: new Abstract: Personalized recommendation depends on learning semantic and collaborative relations among items from online content and user interactions. Generative recommendation (GR)…
arXiv:2610.36973v1 Announce Type: new Abstract: Coordinating multiple recommendation objectives (e.g., click and conversion) is critical for real-world generative recommendation (GR). However, since a positive item in…
arXiv:2610.36763v1 Announce Type: new Abstract: Generative recommendation (GR) predicts the next item through its semantic ID (SID). Existing methods commonly fit SID vocabularies to pretrained language model (PLM)…
arXiv:2610.37080v1 Announce Type: new Abstract: Web retrieval-augmented generation (RAG) splits a page into fixed-size text chunks or, in recent visual pipelines, screenshot tiles, and the retrieved item serves at once…
arXiv:2608.29249v3 Announce Type: replace-cross Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by LLMs. Although often convincing, such outputs may…
arXiv:2510.18239v5 Announce Type: replace Abstract: Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and increasing model…
arXiv:2610.36557v1 Announce Type: new Abstract: Chunking is a fundamental preprocessing step in retrieval-augmented generation (RAG), yet most existing chunkers rely primarily on heuristics. We introduce the…
arXiv:2610.36389v1 Announce Type: cross Abstract: Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement. However, existing evaluation…
54 items available
