Paper Info
Title
Predicting Session Length for Product Search on E-commerce Platform
Abstract
Estimation of session duration for an e-commerce search engine is important for various downstream applications, including user satisfaction prediction, personalization, and diversification of search results. It has been shown in previous studies that search session length has a strong correlation with user's explore vs specific purchase intent. Based on previous work [14], we hypothesize that early prediction of session length distribution can be used to control the degree of explore vs exploit (loosely related to diversification v/s personalization) for Search Engine Result Pages (SERPs) in the user's session to follow. In this work, we try to early predict the user's session length, which will enable the control on explore v/s exploit of the search results. Towards this end, based on previous work and strong empirical evidence, we hypothesize session lengths are Weibull distributed and propose its parameters being modeled by a Recurrent Neural Network over actions in user's search sessions. Through experimentation, we demonstrate that our method performs better as compared to strong baselines for the same.
Year
DOI
Venue
2020
10.1145/3397271.3401219
SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-8016-4
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
shashank gupta16011.35
Subhadeep Maji201.35