build personalization engines

This work addresses the limitations of existing personalized services, which are typically confined to single platforms and unable to effectively integrate heterogeneous user data across multiple online platforms and offline contexts, resulting in incomplete user profiles. To overcome this challenge, the paper introduces a novel user-centric paradigm for cross-domain personalization that places the user at the center of the process. By leveraging readily available large language model (LLM) agents, the proposed approach intelligently reasons over and fuses multi-source data voluntarily exported by users themselves, thereby breaking down platform-specific data silos. Experimental results demonstrate that this method significantly outperforms baseline approaches relying solely on single-platform data, confirming the effectiveness and superiority of a user-controlled, boundary-spanning personalization system.

This survey addresses personalized generation (PGen)—the multimodal content creation tailored to user preferences and requirements—in the era of large language and foundation models. We propose the first unified conceptual framework, formally defining its core components, objectives, and abstract workflow. A hierarchical taxonomy is introduced, spanning text, image, audio, and other modalities while jointly considering personalization contexts and task types. We systematically review technical advances, benchmark datasets, and evaluation metrics. Our analysis identifies critical challenges, including cross-modal coordination and dynamic preference modeling, and highlight key future directions: interpretability, privacy-preserving personalization, and standardized evaluation protocols. As the inaugural comprehensive, structured, and extensible reference for PGen, this work bridges academic research and industrial practice across disciplines, enabling rigorous, reproducible, and ethically grounded development of personalized generative systems.

Manual orchestration in cross-channel marketing leads to insufficient personalization in content, timing, frequency, and copy. Method: This paper proposes an agent-driven, causally enhanced sequential decision-making framework. It introduces a novel strategy optimization mechanism integrating Difference-in-Differences (DID) causal effect estimation with Thompson sampling, enabling interpretable and scalable personalized decisions; and designs a modular policy network supporting joint modeling and coordinated optimization across multi-touch channels—including email, push notifications, and in-app messaging. Contribution/Results: Evaluated in a production environment with 150 million users, the system significantly improves incremental engagement rates across all funnel stages and boosts target-event conversion rates. It establishes a new paradigm for large-scale, causally grounded personalized marketing.

This study addresses critical limitations in existing user modeling approaches within model-driven engineering, including fragmentation, incomplete dimensional coverage, and weak dynamic evolution capabilities, compounded by a lack of effective tool support. To overcome these challenges, the authors propose a low-code-driven unified user modeling framework that integrates multidimensional domain knowledge to construct reusable user models. Leveraging machine learning, the framework enables dynamic, incremental updates and automatic adaptation of user profiles. Grounded in a systematic literature review, user behavior analysis, and automated pipeline technologies, this work not only identifies current modeling shortcomings but also outlines a technical roadmap for developing dynamic, comprehensive, and automated personalized dialogue systems, thereby establishing a novel paradigm for efficient personalization in intelligent agents.

Contemporary recommender systems personalize content using user attributes or inferred data but suffer from poor explainability and auditability, hindering users’ ability to make informed privacy decisions and undermining algorithmic accountability. This paper introduces the first lightweight, privacy-preserving, end-user–oriented algorithmic auditing paradigm: an interactive sandbox that enables users to actively formulate hypotheses—via synthetically generated user profiles and behavioral data—and observe system responses (e.g., ad delivery) in real time, transforming black-box attribution into a verifiable hypothesis-testing process. The approach integrates synthetic data modeling, a user-facing sandbox interface, and an A/B-style response observation framework. A user study demonstrates significant improvements in users’ comprehension of recommendation logic, attribution accuracy, and privacy decision-making capability; in advertising scenarios, hypothesis validation success reached 92%.

This work addresses the limitations of traditional recommender systems, which rely on platform-owned implicit user profiles that lack transparency, editability, and cross-platform portability, thereby limiting users’ understanding and control over their personalized experiences. The paper introduces a novel paradigm—“governable personalization”—and presents the first systematic architecture centered on large language model (LLM) agents for recommendation. By integrating cross-domain memory, privacy-preserving modeling, intent alignment, and trustworthy monetization mechanisms, the proposed framework empowers users to inspect, revise, transfer, and exert influence over their profiles across services. This study establishes foundational design principles and a research agenda for recommender systems in the LLM era, offering both theoretical grounding and technical pathways toward user-driven personalization.

Existing black-box auditing methods struggle to disentangle user attributes from behavior while preserving behavioral authenticity, limiting causal understanding of personalization algorithms. This study introduces the first large-scale algorithmic audit using generative AI agents: leveraging real survey data, it constructs synthetic accounts with fixed personas that autonomously interact on the X platform, enabling counterfactual experiments through controlled manipulation of visible user attributes. This approach effectively decouples attributes from behavior, supporting fine-grained, scalable, and behaviorally authentic analysis. An empirical deployment of 1,120 agents reveals that the platform’s algorithms significantly amplify toxic, polarizing, and right-leaning political content, with amplification effects varying by user ideology; furthermore, the influence of demographic signals exhibits substantial heterogeneity across subpopulations.

This study addresses the limitations of traditional CRM systems, which rely on manual optimization of static rules and struggle to sustain personalized marketing effectiveness over time without ongoing human intervention. Through an 11-month longitudinal case study, the authors propose a human–agent symbiotic model in which humans initially lead strategy formulation and content design, after which an autonomous agent-based system operates independently. Employing a fixed component library and longitudinal A/B testing, the research demonstrates that the human-led phase significantly boosts user engagement metrics, while the subsequent fully autonomous phase continues to maintain positive effects. These findings empirically validate the capacity of agent-based systems to sustain marketing performance over the long term without continuous human oversight, offering robust evidence for the viability and sustainability of human–agent collaboration in personalized marketing.

This work addresses the lack of a precise definition of “personalization” in existing algorithmic recourse methods, which hinders systematic evaluation of its impact on effectiveness, cost, and reasonableness. The paper formalizes personalization as individualized actionability by incorporating hard constraints—restricting the set of actionable features—and soft constraints—modeling users’ preferences over the value and cost of recommended actions—within a causal recourse framework. It further introduces a pre-recourse user prompting mechanism to enable personalized recommendations. Experimental results demonstrate that hard constraints substantially reduce both the effectiveness and reasonableness of recourse suggestions. Moreover, significant disparities emerge across social groups in terms of recourse cost and reasonableness, revealing a complex trade-off between personalized design and fairness.