On the Approaches to Triggering Serendipity in Recommender Systems and Their Impacts to User Satisfaction
Date Issued
2012
Date
2012
Author(s)
Lin, Yu-Hsuan
Abstract
This study focuses on two main recommender paradigms: collaborative-filtering and content-based, and introduces the “Role of chance” approach and the “Anomalies and exceptions” approach. The above two approaches are integrated in this study to form a theoretical model that examines their effects on triggering serendipity and the subsequent effects on several metrics such as user satisfaction and willingness to pay. An experiment was conducted to test the model. Participants were grouped by each recommender conditions and were asked to make a purchase at a simulated online retailer. After the experiment, participants were asked to complete a survey to report their interest, satisfactory and willingness to pay levels. Results indicate that there might be a trade-off relationship between serendipity and other metrics. In addition, collaborative-filtering recommenders which adopted the “Anomalies and exceptions” approach seem to be the most suitable combination to introduce serendipity. Finally, setting a threshold to filter products among recommendation candidates such as high rating would ease the trade-off. Our findings have major implications for the ongoing research on serendipity of recommendations.
Subjects
Recommendation systems
Serendipity
User satisfaction
Willingness to Pay
Role of Chance
Anomalies and Exceptions
Type
thesis
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