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General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
,
etc
.
Acceptance Rate:
19%
APC:
500 USD
Average Days to Accept:
135 days
Journal Metrics:
Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
Home
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Published Issues
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2018
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Volume 9, No. 4, November 2018
>
Predicting Pregnant Shoppers Based on Purchase History Using Deep Convolutional Neural Networks
Nikhil Angad Bakshi
1
, Prithviraj Reddy Kolan
1
, Bibek Behera
1
, Naveen Kaushik
2
, and Ansari Mohammed Ismail
3
1. Sears Holdings Corporation, Pune, MH, India
2. Sears Holdings Corporation, Bengaluru, KA, India
3. Sears Holdings Corporation, Seattle, WA, USA
Abstract
—Predicting pregnant shoppers based on their transaction history and purchasing trends is a challenging problem because of data sparsity and imbalance. Not only are the instances, i.e., the ratio of pregnant versus non-pregnant shoppers, skewed but also is the proportion of the products that reveal pregnancy status. The problem of class imbalance has been solved by taking an equal number of positive and negative examples for training and testing while deployment has been done on the entire dataset yielding results that were congruent with the real-world. In this paper, we use a novel approach that uses deep Convolutional Neural Networks (CNNs) to handle one dimensional data. The proposed solution overcomes the above mentioned challenges and proves that two dimensional CNNs outperform a baseline LightGBM (gradient boosting framework that uses tree based learning algorithms) model on two different datasets - the dataset based on twenty one
hot products
and the dataset based on all products by subcategory. The CNN model reached an F1 score of 0.69 on the test set.
Index Terms
—convolutional neural networks, pregnancy detection, retail prediction, deep learning, pregnant shopper behavioral trends
Cite: Nikhil Angad Bakshi, Prithviraj Reddy Kolan, Bibek Behera, Naveen Kaushik, and Ansari Mohammed Ismail, "Predicting Pregnant Shoppers Based on Purchase History Using Deep Convolutional Neural Networks," Vol. 9, No. 4, pp. 110-116, November 2018. doi: 10.12720/jait.9.4.110-116
5-D106
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