arXiv:2607. 01530v1 Announce Type: cross Abstract: Understanding user intent is fundamental to delivering relevant search results in e-commerce.
arXiv:2607. 27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall.
The paper introduces a generative query suggestion framework that focuses on covering distinct user intents while ensuring each suggested query is useful. It employs a two‑stage optimization: first, intent‑aware diversity modeling creates supervised fine‑tuning data and a reward that encourages intent coverage; second, query‑level credit assignment directs quality signals to individual query tokens while sharing a slate‑level diversity signal. Experiments on a large production dataset, including online A/B tests, demonstrate gains in click‑through rate, query quality, and intent coverage.
arXiv:2607. 03886v1 Announce Type: cross Abstract: Sponsored search plays a crucial role as a revenue stream for search engines, wherein advertisers competitively bid on keywords that align with the users' search queries.
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items.
The paper proposes a single hierarchical Semantic ID (SID) system to unify product identification across multiple merchants in e-commerce. By learning SID representations from product content, the authors demonstrate that ranking algorithms can aggregate consumer affinity and product performance over SID prefixes, improving offline relevance and online engagement. For query reformulation, SID concepts guide navigation and refinement, yielding better intent preservation and higher-quality suggestions compared to taxonomy or raw query transitions.
arXiv:2607. 03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries.
arXiv:2606. 26277v1 Announce Type: cross Abstract: Sequential user behavior modeling is widely adopted in industrial recommender systems; however, significant gaps remain in financial services, where pre-login web interactions and authenticated in-app experiences differ drastically.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...
The study examines two behavioral inference tasks—session-level user identification and next-domain prediction—using large-scale anonymous web browsing traces. Classical and neural models are applied to user identification, while graph-based methods combined with Large Language Models (LLMs) are used for next-domain prediction. Results show that short browsing sessions are highly identifiable and future navigation is highly predictable, with LLM-derived semantic features offering only marginal improvements over structural and sequential models.
The paper describes a hybrid search system developed at Target that combines lexical and vector search to improve retail product search. It details data processing, embedding training, precision control, multi‑channel result fusion—specifically weighted interleaving—and performance optimizations for low latency. The system achieved measurable gains in click‑through rate, order conversion, and demand per visitor while reducing zero‑result searches.
arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.
arXiv:2606. 18897v1 Announce Type: cross Abstract: Intent-based recommender systems have gained significant attention for improving accuracy and interpretability by modeling the underlying motivations behind user behaviors.