Paper Info
Title
Adaptive, Personalized Diversity for Visual Discovery.
Abstract
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we explore extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon. Our system presents the user with a diverse set of interesting items while adapting to user interactions. Our solution consists of three components (1) a Bayesian regression model for scoring the relevance of items while leveraging uncertainty, (2) a submodular diversification framework that re-ranks the top scoring items based on category, and (3) personalized category preferences learned from the user's behavior. When tested on live traffic, our algorithms show a strong lift in click-through-rate and session duration.
Year
DOI
Venue
2018
10.1145/2959100.2959171
RecSys
Keywords
DocType
Volume
Machine Learning, Submodular Functions, Diversity, Personalization, Explore-Exploit, Multi-Armed Bandits
Journal
abs/1810.01477
ISSN
Citations 
PageRank 
Adaptive, Personalized Diversity for Visual Discovery. Teo CH, Nassif H, Hill D, Srinavasan S, Goodman M, Mohan V, and Vishwanathan SVN. ACM Conference on Recommender Systems (RecSys'16), Boston, pp. 35-38, 2016
15
0.58
References 
Authors
9
7
Name
Order
Citations
PageRank
Choon Hui Teo162347.52
Houssam Nassif2889.75
Daniel Hill3150.58
Sriram Srinivasan437927.92
Mitchell Goodman5150.58
Vijai Mohan6150.58
S. V. N. Vishwanathan71991131.90