Paper Info
Title
Scalable Image-based Search-and-Discovery.
Abstract
People use online search-and-discovery services, such as Yelp, by first finding a specific item with keywords and then examining the images linked to the item. Images could constitute an important part of users' decision-making process but users reach them indirectly. Although recently researchers proposed several image-based search interfaces, how they can effectively arrange huge number of images in a scalable manner is still not clear. To address this, we introduce PicNav, an image-driven navigation system that automatically arranges photos according to their semantic similarity. PicNav is built on deep neural networks learned from the Yelp food dataset and enables effective zoom-in/out features. We conducted interviews with ten users to qualitatively assess the system's usability. The users identified a number of advantages of PicNav, providing insights into the general use of imagery in search-and-discovery services.
Year
DOI
Venue
2017
10.1145/3027063.3053136
CHI Extended Abstracts
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
3
4
Name
Order
Citations
PageRank
Eunji Chong1152.89
Jaehoon Lee2347.85
Matthew Hong322.94
James M. Rehg45259474.66