Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
원문 초록
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Specifically, we present our system architecture that utilizes popular recommendation algorithms from the literature such as Collaborative Filtering, Content-based Filtering as well as various neural embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We showcase the applicability of our system architecture using two real-world use-cases, namely providing recommendations for the domains of (i) job marketplaces, and (ii) entrepreneurial start-up founding. We strongly believe that our experiences from both research- and industry-oriented settings should be of interest for practitioners in the field of real-time multi-domain recommender systems.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
토론
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