ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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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…

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.
ContributorsVaidya, Yogesh Vinayak (Author) / Bansal, Ajay (Thesis advisor) / Findler, Michael (Committee member) / Chakravarthi, Bharatesh (Committee member) / Arizona State University (Publisher)
Created2023