Paper Info
Title
Optimizing Whole-Page Presentation for Web Search.
Abstract
Modern search engines aggregate results from different verticals: webpages, news, images, video, shopping, knowledge cards, local maps, and so on. Unlike “ten blue links,” these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional “ranked list” formulation in ad hoc search. Therefore, finding proper presentation for a gallery of heterogeneous results is critical for modern search engines. We propose a novel framework that learns the optimal page presentation to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content aware, i.e., tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can learn its own result presentation strategy purely from data, without even knowing the “probability ranking principle.”
Year
DOI
Venue
2018
10.1145/3204461
TWEB
Keywords
Field
DocType
Whole-page optimization, user satisfaction
Federated search,Search engine,Ranking,Web page,Information retrieval,Computer science
Journal
Volume
Issue
ISSN
12
3
1559-1131
Citations 
PageRank 
References 
2
0.50
52
Authors
7
Name
Order
Citations
PageRank
Yue Wang125.57
Dawei Yin286661.99
Luo Jie3251.28
Pengyuan Wang4505.43
Makoto Yamada545943.38
Yi Chang6146386.17
Qiaozhu Mei74395207.09