Tag
Elon Musk retweeted Keith Coleman's explanation of an algorithm using a simple analogy for a 5-year-old, comparing it to a smart robot selecting toys for a feed.
This paper introduces Knowledge-Geometry Decoupling (KGD), a method for pretrain-then-transfer in streaming recommendation systems. It separates pretrained behavioral knowledge from task-specific geometry, enabling continual model refresh without interference, and reports 4-12% improvements over baselines plus successful deployment at Shopee.
An opinion piece arguing that 'personalized' recommendations are not truly personal but based on behavioral clustering, and that applying the same architecture across different product categories is lazy AI design.
This survey comprehensively reviews GNN-based link prediction from a dedicated GNN perspective, categorizing recent advancements by techniques (GCN, GAE, GAT, GFormer) and applications (knowledge graphs, recommendation systems), and discusses challenges and future directions.
This paper investigates how providing users with transparency and control over a political news recommendation system affects filter bubbles. A user study found that the enhanced interface increased awareness of filter bubbles but had heterogeneous effects on news consumption diversity.
This paper presents a reproducible pipeline for extracting structured user-preference triples from conversational data using lightweight LLMs for personal knowledge graph construction and evaluates the downstream utility in recommendation systems.
The quality of recommendations made by AI agents is dependent on the resources of the merchants behind them.
This paper presents a study examining how anthropomorphic language in AI discourse affects public perceptions, finding that while overall views can shift, the specific effect of anthropomorphic framing is modest in controlled settings.
The article argues that before monetizing AI agents with a payment layer, a trust layer must be established to ensure transparency and reliability in recommendations and transactions.
This article discusses recommendation systems powered by AI agents.
The article discusses how AI agents are transforming business discovery, moving beyond traditional search ad interfaces to incorporate conversations, recommendations, and tool calls, requiring new infrastructure for tracking and attribution.
This paper presents a contextual multi-armed bandit framework that learns individual spillover probabilities in social networks to optimize stimulated word-of-mouth marketing, achieving higher rewards by targeting connected users.
This paper formalizes embedding model routing as an adversarial contextual linear bandit with low-rank experts, proposing the Hypentropy Policy Gradient (HPG) algorithm that achieves O~(s√(MT)) policy regret, avoiding the curse of dimensionality.
This paper proposes Representation Curriculum (RC), a training-time intervention that stages feature utilization to reduce over-reliance on exposure-confounded historical signals and improve cold-start generalization in ranking systems. The method is theoretically analyzed and validated on public benchmarks and large-scale eBay search experiments.
This paper studies scaling laws for behavioral foundation models trained on sequences of user actions, finding that a small event embedder is compute-optimal and that the evaluation metric itself influences the optimal compute allocation.
This paper models the interaction between a user and an AI-driven recommendation system, analyzing optimal communication and recommendation set sizes under different sampling schemes to maximize expected utility.
Explores whether AI agents can learn from rejected recommendations without compromising user privacy or becoming overly personalized to unique past behaviors.
Discusses the challenges AI agents face when recommending products from multiple information sources, each with its own biases and limitations, and questions how to design a trust layer for reliable recommendations.
A software engineer asks for strategies to bootstrap personalization for new users with no behavioral data, discussing the cold-start problem in content recommendation.
This article explores the question of who should be held responsible when AI agents provide incorrect suggestions, considering the roles of developers, model providers, data suppliers, platforms, and users, and raises key issues for building a trustworthy agent ecosystem.