Topic - Edition
arXiv:2611.05900v1 Announce Type: new Abstract: Personalized retrieval-augmented generation requires extracting and applying user preferences from extended interaction histories. This task is challenging because…
arXiv:2611.06859v1 Announce Type: cross Abstract: Hierarchical text embedding models offer variable-length representations, while quantization reduces numerical precision; both aim to improve computational efficiency.…
arXiv:2611.05997v1 Announce Type: cross Abstract: Helping users discover new interests is vital to the long-term health of industrial recommender systems, yet the usual remedy, boosting exposure of under-explored…
arXiv:2611.06610v1 Announce Type: cross Abstract: Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications. CF uses user-item interaction data to…
arXiv:2611.05664v1 Announce Type: cross Abstract: A neuro-symbolic system should combine only relevant answers from expert agents or other knowledge sources. Using only relevant experts for a given input query becomes…
arXiv:2611.05952v1 Announce Type: new Abstract: Financial asset recommendation for retail investors is typically optimized for relevance, that is, how well a recommendation matches what each user will buy. From the…
arXiv:2611.05193v2 Announce Type: replace-cross Abstract: Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text. Although recent approaches leveraging Multimodal…
arXiv:2611.06521v1 Announce Type: new Abstract: Sequential recommenders are widely applied in real-world scenarios to model user preferences and behavior patterns based on user interactions. Recent advances in…
arXiv:2611.06044v1 Announce Type: new Abstract: Search-augmented recommenders can use the latest search episode and earlier searches. We introduce FocalSwap to test how these inputs contribute to the next…
arXiv:2611.05908v1 Announce Type: new Abstract: Second-stage neural rankers are commonly applied with a fixed re-ranking depth, assuming that retrieval effectiveness saturates as depth increases. Prior research has…
arXiv:2606.00044v1 Announce Type: cross Abstract: The integration of artificial intelligence into clinical medicine creates a fundamental tension between algorithmic probabilistic reasoning and the experiential…
arXiv:2611.06777v1 Announce Type: new Abstract: Algorithm-driven services, including search, recommendation, and advertising, rely on multi-stage model pipelines. While research prioritizes model optimization,…
arXiv:2608.16586v2 Announce Type: replace Abstract: Dense retrieval is commonly evaluated on benchmarks that represent each document with a single embedding, even though real-world retrieval systems often index long…
arXiv:2611.05753v1 Announce Type: cross Abstract: Organizations accumulate evidence in conversations, documents, decisions and relationships, but they act on interpretations: judgements assembled from several…
arXiv:2608.24034v2 Announce Type: replace Abstract: Live-streaming advertising is an important monetization channel on short-video and e-commerce platforms, where rapidly changing live content, promoted products, and…
arXiv:2611.06323v1 Announce Type: new Abstract: Generative retrieval predicts semantic identifiers for candidate entities. In local retail and food delivery, candidates must also be serviceable for a particular user at…
arXiv:2611.05829v1 Announce Type: new Abstract: Researchers frequently encounter difficulties re-finding scientific papers when they remember aspects of the content but cannot recall reliable identifiers, such as titles…
arXiv:2508.01128v2 Announce Type: replace Abstract: Textual reviews enrich recommender systems with fine-grained preference signals and enhanced explainability. However, in real-world scenarios, users rarely leave…
arXiv:2607.28685v2 Announce Type: replace-cross Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety. We treat four of them (R-Judge,…
arXiv:2611.05445v1 Announce Type: cross Abstract: Large language model agents increasingly retrieve tools from large catalogs, yet most retrievers rank each tool independently by request similarity. For multi tool…
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