TY - GEN
T1 - Numerical facet range partition
T2 - 26th International World Wide Web Conference, WWW 2017 Companion
AU - Liu, Xueqing
AU - Zhai, Chengxiang
AU - Han, Wei
AU - Gungor, Onur
N1 - Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
PY - 2017
Y1 - 2017
N2 - Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.
AB - Faceted navigation is a very useful component in today's search engines. It is especially useful when user has an exploratory information need or prefer certain attribute values than others. Existing work has tried to optimize faceted systems in many aspects, but little work has been done on optimizing numerical facet ranges (e.g., price ranges of product). In this paper, we introduce for the first time the research problem on numerical facet range partition and formally frame it as an optimization problem. To enable quantitative evaluation of a partition algorithm, we propose an evaluation metric to be applied to search engine logs. We further propose two range partition algorithms that computationally optimize the defined metric. Experimental results on a two-month search log from a major e-Commerce engine show that our proposed method can significantly outperform baseline.
KW - Faceted search
KW - Information retrieval models
KW - Non-smooth optimization
KW - User search log
UR - http://www.scopus.com/inward/record.url?scp=85049578677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049578677&partnerID=8YFLogxK
U2 - 10.1145/3041021.3054195
DO - 10.1145/3041021.3054195
M3 - Conference contribution
AN - SCOPUS:85049578677
T3 - 26th International World Wide Web Conference 2017, WWW 2017 Companion
SP - 662
EP - 671
BT - 26th International World Wide Web Conference 2017, WWW 2017 Companion
Y2 - 3 April 2017 through 7 April 2017
ER -