Query-based simple and scalable recommender systems with apache hivemall
Takuya Kitazawa, Makoto Yui · 2018
This study demonstrates a way to build large-scale recommender systems by just writing a series of SQL-like queries. In order to efficiently run recommendation logics on a cluster of computers, we implemented a variety of recommendation algorithms and common recommendation functions (e.g., efficient similarity computation, top-k retrieval, and evaluation measures) asHive user-defined functions (UDFs) in Apache Hivemall. We demonstrate that how Apache Hivemall can easily be used for building a scalable recommendation system with satisfying business requirements such as scalability, latency, and stability.