Digital and Mobile Commerce
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Paper Number
2629
Paper Type
short
Description
Recommender systems enable users to discover items of interest from a large set of alternatives. Most recommender systems employ accuracy-oriented algorithms to predict user preferences. Overemphasis on accuracy leads to monotony in the items predicted, resulting in low customer satisfaction. Hence, to improve user experience, it is crucial to inject serendipity into recommendations by discovering users’ latent preferences and recommending items that are both relevant and unexpected. However, there is a lack of serendipity-related labelled data and previous serendipitous recommendation algorithms are unable to strike a trade-off between serendipity and accuracy. We address the challenges by presenting a new dataset and designing a novel deep learning-based recommendation algorithm. Our experiments demonstrate that our model improves over state-of-the-art methods, in both recommendation accuracy and serendipity. We outline our plan to conduct a comprehensive user evaluation, through surveys and eye-tracking experiments, to understand how serendipitous recommendations impact user behavior in e-commerce.
Recommended Citation
Cui, Wei; Rajan, Vaibhav; and Jiang, Zhenhui, "Expect the Unexpected: Engaging Users via Serendipitous Recommendations" (2021). ICIS 2021 Proceedings. 14.
https://aisel.aisnet.org/icis2021/digital_commerce/digital_commerce/14
Expect the Unexpected: Engaging Users via Serendipitous Recommendations
Recommender systems enable users to discover items of interest from a large set of alternatives. Most recommender systems employ accuracy-oriented algorithms to predict user preferences. Overemphasis on accuracy leads to monotony in the items predicted, resulting in low customer satisfaction. Hence, to improve user experience, it is crucial to inject serendipity into recommendations by discovering users’ latent preferences and recommending items that are both relevant and unexpected. However, there is a lack of serendipity-related labelled data and previous serendipitous recommendation algorithms are unable to strike a trade-off between serendipity and accuracy. We address the challenges by presenting a new dataset and designing a novel deep learning-based recommendation algorithm. Our experiments demonstrate that our model improves over state-of-the-art methods, in both recommendation accuracy and serendipity. We outline our plan to conduct a comprehensive user evaluation, through surveys and eye-tracking experiments, to understand how serendipitous recommendations impact user behavior in e-commerce.
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Comments
22-Digital