AOL4PS: A Large-scale Data Set for Personalized Search
Qian Guo, Wei Chen, Huaiyu Wan
Abstract
Personalized search is a promising way to improve the quality of Websearch, and it has attracted much attention from both academic and industrial communities. Much of the current related research is based on commercial search engine data, which can not be released publicly for such reasons as privacy protection and information security. This leads to a serious lack of accessible public data sets in this field. The few publicly available data sets have not become widely used in academia because of the complexity of the processing process required to study personalized search methods. The lack of data sets together with the difficulties of data processing has brought obstacles to fair comparison and evaluation of personalized search models. In this paper, we constructed a large-scale data set AOL4PS to evaluate personalized search methods, collected and processed from AOL query logs. We present the complete and detailed data processing and construction process. Specifically, to address the challenges of processing time and storage space demands brought by massive data volumes, we optimized the process of data set construction and proposed an improved BM25 algorithm. Experiments are performed on AOL4PS with some classic and state-of-the-art personalized search methods, and the experiment results demonstrate that AOL4PS can measure the effect of personalized search models.