Full metadata
Title
A/B Testing-based Recommendation Systems
Description
Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train
them and give recommendations to the users. These algorithms need a large amount of data
for a reasonable conversion rate. But for small applications, the available amount of data
is minimal, leading to high recommendation aberrations. Also, when an existing large
scaled application with a high amount of available data uses a new recommendation
system, it requires some time and testing to decide which recommendation algorithm is
best suited to get higher conversion rates. This learning curve costs highly when the user
base and data size are significantly high.
In this thesis, A/B testing is used with manual intervention in the decision-making
of recommendation systems. To understand the effectiveness of the recommendations, user
interaction data is compared to compare experiences. Based on the comparisons, the
experiments conclude the effectiveness of A/B testing for the recommendation system.
Date Created
2023
Contributors
- Vaidya, Yogesh Vinayak (Author)
- Bansal, Ajay (Thesis advisor)
- Findler, Michael (Committee member)
- Chakravarthi, Bharatesh (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
87 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.187340
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2023
Field of study: Software Engineering
System Created
- 2023-06-06 07:20:28
System Modified
- 2023-06-06 07:20:32
- 1 year 5 months ago
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