SPADE (Serendipitous Pareto Distance Evaluation) is a new metric for recommender systems that simultaneously considers item similarity, popularity, and user relevance. It projects items into a two‑dimensional space and computes a user‑specific Pareto frontier of maximally popular and historically similar items, then averages the minimum Euclidean distance from this frontier for correctly recommended test‑set items. Experiments on five datasets and five baseline algorithms demonstrate that SPADE effectively discourages algorithms from exploiting accuracy‑only metrics and reliably isolates serendipitous discoveries.
By Tobias Vente, Maarten Peirsman, Noah Dani\"els, Hannu Toivonen, Bart Goethals
arXiv:2606. 26369v1 Announce Type: cross Abstract: Scoring functions are used to represent the relevance of individual documents.
By Shubham Singh, Ian A. Kash, Mesrob I. Ohannessian
arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.
By Ronald Sielinski
arXiv:2608. 11390v1 Announce Type: new Abstract: Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value.
By Chen Xu, Zitian Guo, Chenyan Xiong
The paper proposes an incremental recommendation approach that uses a causal model built from existing holdback data to avoid delivering redundant recommendations. By applying a dual‑threshold targeting policy, the system only recommends content when the likelihood of a treated stream is high and the likelihood of an organic stream is low, thereby reducing recommendation impressions by 7% without hurting overall consumption. Joint training with holdback data also improves the calibration of the treated head, suggesting that causal models capture more generalisable representations than purely observational models.
ZooWork-ShopRanker is a family of open e‑commerce rerankers (0.6B, 4B, and 8B) that align with human shopping preferences by using large language models as preference oracles to generate training pairs. The flagship 8B model serves as a teacher for the smaller 4B and 0.6B models, which are further refined on judged pairs. A new benchmark, ShopRank‑Bench, contains ~10,000 private‑traffic preference pairs and shows that all ZooWork models outperform the strongest open reranker baseline and their own un‑aligned versions.
By Siqiao Xue, Shuxuan Liu, Ning Hu
arXiv:2608.30466v1 Announce Type: new
Abstract: Generative Engine Optimization (GEO) is increasingly used to improve content visibility in LLM-based retrieval systems, yet its population-level effect...
By Qianwen Gao, Zichang Su, Yiwen Hou, Arlen Kumar, Leanid Palkhouski
arXiv:2606. 15146v1 Announce Type: new Abstract: Stimulated word-of-mouth is a strategy that promotes information sharing through prompts or incentives.
By Ahmed Sayeed Faruk, Elena Zheleva
The paper introduces Connected Content Retriever (CC Retriever), a pre‑ranking system for LinkedIn’s Feed that uses dense graph edge features to score candidate content from a billion‑scale index within a 120 ms latency budget. By leveraging GPU‑based sorted‑search primitives, the system can apply a full deep ranking model with 50× more parameters, achieving a 2.5% lift in content time spent in online experiments. The work details the economic‑graph features and model architecture that enable this scalable, low‑latency scoring pipeline.
By Akhilesh Gupta, Sudarshan Srinivasa Ramanujam, Chirag Bhanuprasad Mehta, Reshma Asharaf Beena, Dhritiman Das, Birjodh Singh Tiwana, Bhargavkumar Kanubhai Patel, Mack Lee, Renyi Tang
arXiv:2508. 11847v4 Announce Type: replace-cross Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a worst-case very small fraction of preference data.
By Jenny Y. Huang, Yunyi Shen, Dennis Wei, Tamara Broderick
arXiv:2607. 14418v1 Announce Type: new Abstract: Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes.
By Mohammad Rashid, Hema Yoganarasimhan
arXiv:2609.23877v1 Announce Type: new
Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users fre...
By Xiao Liu, Yanwei Song, Srivaths Ranganathan, Yuan Chen, Zheyun Feng, Parker Steenburgh, Jochen Klingenhoefer, Nathan Lasche, Gergo Varady, Tim Steele