Recommendations for two-way selections using skyline view queries

Jian Chen, Jin Huang, Bin Jiang, Jian Pei, Jian Yin

Abstract: We study a practical and novel problem of making recommendations between two parties such as applicants and job positions. We model the competent choices of each party using skylines. In order to make recommendations in various scenarios, we propose a series of skyline view queries. To make recommendations, we often need to answer skyline view queries for many entries in one or two parties in batch, such as for many applicants versus many jobs. However, the existing skyline computation algorithms focus on answering a single skyline query at a time and do not consider sharing computation when answering skyline view queries for many members in one party or both parties. To tackle the batch recommendation problem, we develop several efficient algorithms to process skyline view queries in batch. The experiment results demonstrate that our algorithms significantly outperform the state-of-the-art methods.