Product Recommendation
1개 벤치마크 · 논문 149편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Representation Learning for Attributed Multiplex Heterogeneous Network
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Retrieving Similar E-Commerce Images Using Deep Learning
Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
Papers
Beyond Co-purchase Relation: Evolution of Complementary Recommendations at Allegro
When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic tripod, or another camera body? Complementary Product Recommendation is vital for comprehensive basket buildi…
Product RecommendationOne More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies
Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask, what to ask, when to stop, and when to answer. We introduce RegretBench, a mult…
Product RecommendationTrading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations
E-commerce recommender systems strongly influence which products users consider and purchase, yet sustainability signals such as Product Carbon Footprint (PCF) are almost never available at catalog scale. We study carbon…
Product RecommendationSemantic SimilarityEcoGEO: Trajectory-Aware Evidence Ecosystems for Web-Enabled LLM Search Agents
Web-enabled LLM agents are changing how online information influences search outcomes. Existing Generative Engine Optimization (GEO) studies mainly focus on individual webpages. However, agentic web search is not a singl…
Product RecommendationValue-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each prod…
Product RecommendationBeyond Interleaving: Causal Attention Reformulations for Generative Recommender Systems
Generative Recommender Systems (GR) increasingly model user behavior as a sequence generation task by interleaving item and action tokens. While effective, this formulation introduces significant structural and computati…
Product Recommendation