Exact and Approximate Maximum Inner Product Search with LEMP

• Autores: Christina Teflioudi, Rainer Gemulla
• Localización: ACM transactions on database systems, ISSN 0362-5915, Vol. 42, Nº 1 (Invited Paper from ICDT 2014, Invited Paper from EDBT 2015, Regular Papers and Technical Corresponde), 2017
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • We study exact and approximate methods for maximum inner product search, a fundamental problem in a number of data mining and information retrieval tasks. We propose the LEMP framework, which supports both exact and approximate search with quality guarantees. At its heart, LEMP transforms a maximum inner product search problem over a large database of vectors into a number of smaller cosine similarity search problems. This transformation allows LEMP to prune large parts of the search space immediately and to select suitable search algorithms for each of the remaining problems individually. LEMP is able to leverage existing methods for cosine similarity search, but we also provide a number of novel search algorithms tailored to our setting. We conducted an extensive experimental study that provides insight into the performance of many state-of-the-art techniques—including LEMP—on multiple real-world datasets. We found that LEMP often was significantly faster or more accurate than alternative methods.