Abstract
The queries in search engine that issued by users are often ambiguous. By returning diverse ranking results we can satisfy different information needs as far as possible. Recently, a hierarchical structure are proposed to represent user intents instead of a flat list of subtopics. Although the hierarchical diversification model performs better than previous models, it utilizes a predefined function to calculate the diversity score, which may not reach the optimal result. The model’s parameters need to be tuned manually and repeatedly without intention, which cause a time-consuming problem. In this paper, we introduce a learning based hierarchical diversification model. Benefit from the learning model, the parameter values are determined automatically and more optimal. Experiments show that our approach outperform several existing diversification models significantly.
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Ackonwloedgements
This research is supported by National Natural Science Foundation of China (Grant No. 61375054), Natural Science Foundation of Guangdong Province Grant No. 2014A030313745, Basic Scientific Research Program of Shenzhen City (Grant No. JCYJ20160331184440545), and Cross fund of Graduate School at Shenzhen, Tsinghua University (Grant No. JC20140001).
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Zheng, HT., Wang, Z., Xiao, X. (2017). A Learning Approach to Hierarchical Search Result Diversification. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_25
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DOI: https://doi.org/10.1007/978-3-319-63564-4_25
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