TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance
Combining semantic information with behavioral data is a crucial research area in recommender systems. A promising approach involves leveraging external knowledge to enrich behavioral-based recommender systems with abundant semantic information. However, this approach faces two primary challenges: denoising raw external knowledge and adapting semantic representations. To address these challenges, we propose an External Knowledge-Enhanced Recommendation method with LLM Assistance (TRAWL). This method utilizes large language models (LLMs) to extract relevant recommendation knowledge from raw external data and employs a contrastive learning strategy for adapter training. Experiments on public datasets and real-world online recommender systems validate the effectiveness of our approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningDenoisingHallucinationRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application
This work explores unifying knowledge enhanced recommendation with multi-domain recommendation systems in a conversational AI assistant application. Multi-domain recommendation leverages users' interactions in previous d…
Knowledge GraphsRecommendation SystemsLikelihood-based inference and forecasting for trawl processes: a stochastic optimization approach
We consider trawl processes, which are stationary and infinitely divisible stochastic processes and can describe a wide range of statistical properties, such as heavy tails and long memory. In this paper, we develop the …
parameter estimationStochastic OptimizationGNPassGAN: Improved Generative Adversarial Networks For Trawling Offline Password Guessing
The security of passwords depends on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simu…
Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System
We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to ma…
Movie RecommendationJointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation
Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring adv…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsMemorization+1