TY - GEN
T1 - Mobile vertical ranking based on preference graphs
AU - Kadotami, Yuta
AU - Yoshida, Yasuaki
AU - Fujita, Sumio
AU - Sakai, Tetsuya
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/10/1
Y1 - 2017/10/1
N2 - We consider the problem of ranking relevant verticals for a given mobile search query so as to satisfy the average user. To this end, we utilise real mobile search click logs, and apply a graph contruction algorithm proposed by Agrawal et al. who tackled the problem of automatically assigning relevance labels to URLs for general web search. While Agrawal et al. ordered URLs based on pairwise preferences and then partitioned the ordered URL list to determine absolute relevance grades, our objective is to rank a given set of verticals for a given query, to help search engine companies select which verticals to include in a search engine result page for a small smartphone screen. We show that "Click > Skip Other" preference rules consistently outperform more conservative rules such as "Click > Skip Previous" and that our best graph-based vertical ranking methods substantially and statistically significantly outperform a competitive baseline that ranks verticals based on click counts.
AB - We consider the problem of ranking relevant verticals for a given mobile search query so as to satisfy the average user. To this end, we utilise real mobile search click logs, and apply a graph contruction algorithm proposed by Agrawal et al. who tackled the problem of automatically assigning relevance labels to URLs for general web search. While Agrawal et al. ordered URLs based on pairwise preferences and then partitioned the ordered URL list to determine absolute relevance grades, our objective is to rank a given set of verticals for a given query, to help search engine companies select which verticals to include in a search engine result page for a small smartphone screen. We show that "Click > Skip Other" preference rules consistently outperform more conservative rules such as "Click > Skip Previous" and that our best graph-based vertical ranking methods substantially and statistically significantly outperform a competitive baseline that ranks verticals based on click counts.
KW - Click logs
KW - Mobile search
KW - Pairwise preferences
KW - Vertical ranking
UR - http://www.scopus.com/inward/record.url?scp=85033222657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033222657&partnerID=8YFLogxK
U2 - 10.1145/3121050.3121082
DO - 10.1145/3121050.3121082
M3 - Conference contribution
AN - SCOPUS:85033222657
T3 - ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
SP - 225
EP - 228
BT - ICTIR 2017 - Proceedings of the 2017 ACM SIGIR International Conference on the Theory of Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 7th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2017
Y2 - 1 October 2017 through 4 October 2017
ER -