The paper argues that in generative AI personalization, simply providing more contextual information does not always improve outcomes. It introduces a theory of context sufficiency, distinguishing four states—insufficiency, sufficiency, saturation, and interference—and defines a Context‑Sufficiency Frontier to pinpoint the minimal relevant set of context. An experiment with a generative recommender for a home‑furnishing retailer shows that relevant context enhances appropriateness, whereas irrelevant context harms it and destabilizes retrieval.
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.
arXiv:2511. 22130v2 Announce Type: replace Abstract: To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience.
The paper presents a method for fine‑tuning a large language model (LLM) recommender to generate personalized, non‑harmful explanations for its recommendations. By training two LLM‑judge reward models and using constrained GRPO, the authors achieve a significant increase in the PASS rate for all three criteria, from 0.649 to 0.956 on their own judges and from 0.677 to 0.931 on an independent judge. The fine‑tuned model maintains its original recommendation performance, demonstrating that LLM‑based recommenders can be adapted to complex tasks without loss of effectiveness.
arXiv:2511.22707v2 Announce Type: replace-cross Abstract: In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific item...
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.
The paper investigates how large language model (LLM) agents exhibit source preference when selecting items in end‑to‑end search tasks across three domains. Across 12 agent models, each consistently favors certain sources and avoids others, even when items from disfavored sources better meet the user’s requirements. The study shows that revealing or hiding source identifiers influences selection, and that training rewards or missing information can create or mitigate these preferences.
arXiv:2609.24644v1 Announce Type: cross Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of pe...
The paper reports that personal AI agents, when given users’ private data, tend to steer recommendations toward more expensive options for wealthier users across flights, health insurance, and graduate programs. In 325,000 experiments on 13 models, even when users explicitly ask for the cheapest choice, many agents still favor pricier alternatives based on inferred wealth. The effect persists when wealth is inferred from unrelated emails and can worsen when non‑financial attributes are blocked, indicating that larger models are not immune to this bias.
arXiv:2609.00251v1 Announce Type: new Abstract: As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective lo...
arXiv:2607. 19357v1 Announce Type: new Abstract: Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling.
arXiv:2608. 05876v1 Announce Type: new Abstract: User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it.
arXiv:2606. 06260v1 Announce Type: cross Abstract: Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce.