Tag
An analysis of Twitter's community structure using domain co-occurrence data and PCA/t-SNE, revealing tight right-wing and diffuse left-wing clusters, with implications for recommender systems and random walk mixing times.
This paper proposes GOAL, a constraint-aware generative framework for incentivized advertising that formulates incentive allocation as conditional sequence generation, and introduces SCPO to learn a single generative policy that generalizes across ROI constraints. Experiments show improved long-term revenue and user retention with reduced ROI violations.
This paper proposes A/B Agent, a closed-loop agent framework that organizes historical A/B testing knowledge into a hierarchical experience tree, retrieves transferable strategies via multi-path Tree-RAG, and self-evolves through online experiment feedback, achieving a 4.829% GMV improvement in a short-video e-commerce recommendation system.
RecHarness is a bandit-routed agentic harness that automates recommender model optimization by separating direction selection from hypothesis generation, achieving stable improvements and significant gains in an online A/B test.
This paper systematically investigates Semantic IDs (SIDs) in generative recommendation, finding that while SIDs preserve coarse item organization, they lose fine local structure from the encoder. The authors propose Item-Supported Decoding (ISD), a lightweight inference-time method that improves NDCG@10 by up to 31.2% without additional parameters or retraining.
This paper introduces Proximity Features, a privacy-compliant system that uses aggregated geo-IP data to personalize recommendations for cold-start users at Airbnb, achieving significant booking lifts in production experiments.
RouteRec is a framework for evaluating request-level hard selection versus item-level learned aggregation of heterogeneous recommender agents, including an LLM reranker, under cost constraints. Experiments on MovieLens-1M show that item-level aggregation significantly outperforms request-level selection.
This paper analyzes the early training collapse phenomenon in deep neural models for click-through rate prediction and proposes mitigation strategies such as sparse feature removal and value filtering, demonstrating improvements on large-scale industrial datasets.
This paper argues that agents should help users construct preferences rather than assuming well-formed ones, proposing the CoPref model and CoShop benchmark. Evaluations show even frontier models achieve only 56% accuracy due to poor preference expansion.
Proposes a multi-objective reinforcement learning framework combining semantic embeddings with Pareto-DQN to balance engagement, diversity, and fairness in recommendations, mitigating filter bubbles.
OneRank proposes a Transformer-native multi-task ranking framework that integrates feature encoding and prediction to reduce inter-task interference and improve ranking performance in recommender systems.
This paper proposes a carbon-aware re-ranking strategy for e-commerce recommendations, using a retrieval-augmented pipeline to estimate product carbon footprints and trading off predicted engagement against sustainability. Evaluated on Amazon Reviews data, substantial carbon reductions are achievable with minimal engagement loss.
τ-Rec is a verifiable benchmark for agentic recommender systems that replaces subjective LLM-as-a-judge evaluations with verifiable rewards and controlled dialogue constraints, revealing steep reliability cliffs across leading models where even the best achieves only ~57% pass@1.
This paper proposes RankElastor, a novel architecture that mitigates embedding collapse in dense scaling of recommendation models by introducing parameterized full mixing and GLU-improved P-FFNs, achieving robust scaling and improved performance on large-scale datasets.
PEARL introduces a contrastive percentile approximation framework to mitigate behavioral intensity imbalance in recommender systems, achieving significant gains in engagement metrics in a production livestream platform serving billions of users.