CATER: A Cluster-Based Alternative-Term Recommendation Framework for Large-Scale Web Search at NAVER

Abstract

Recently, searching for information by using search engines such as Google, Bing, and NAVER has become ubiquitous. While they attempt to provide information based on the search queries that users enter, it is not trivial to accurately capture the search intent of users. Motivated by this situation, NAVER Corp., the largest portal company in Korea, has developed a framework named as CATER (Cluster-based Alternative TErm Recommendation) framework that suggests alternative terms ("al-terms,'' in short) for better search outcomes relevant to a user's search intent. We introduce four design considerations (DCs) that were considered when designing and implementing CATER. Then, we describe how our CATER addresses the four DCs by using a clustering stage that dynamically maintains a pool of topic-oriented clusters containing terms, and a recommendation stage that identifies the top-k clusters (i.e., topics) and the top-k al-terms for each cluster. Furthermore, we present the scalable architecture adopted by CATER. Through various offline and online A/B tests using real-world datasets from NAVER, we validate that CATER successfully incorporates all DCs and that all design choices help improve the recommendation accuracy.