Abstract
During the shopping process, users typically narrow down their search to a small collection of products before making a final purchase. These data, consisting of products that users are considering purchasing, correlate strongly with user search intent and product desirability. By allowing users to bookmark products between browsing and purchasing, we collect user-interest information. We then propose a product recommendation algorithm based on these data. By considering both popular and long-tail queries, we shed light on the potential usage of the data.
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- 1.
Collections enable users to bookmark products and organized in one place, for example, http://www.ebay.com/cln, or http://www.pinterest.com.
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Acknowledgement
We appreciate the help from Noah Batterson for his work in designing the user interface of this project. In addition, we thank Lan Wang for sharing her insights and help in building the first prototype. Our work is greatly influenced by their knowledge of improving user experience.
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Hsieh, CC., Medoff, Y., Chittar, N. (2014). A Short-Term Bookmarking System for Collecting User-Interest Data. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_24
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DOI: https://doi.org/10.1007/978-3-319-13186-3_24
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