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• W2585519298 abstract "Generally 2% of shoppers make a purchase on the first visit to an online store while the other 98% enjoys only window-shopping. To bring people back to the store and close the deal, retargeting has been a vital online advertising strategy that leads to conversion of window-shoppers into buyers. As such retargeting is more effective as a focused tool, in this paper, we study the problem of identifying a conversion rate for a given product and its current customers, which is an important analytics metric for retargeting process. Compared to existing approaches using either of customer- or product-level conversion pattern, we propose a joint modeling of both level patterns based on the well-studied buying decision process. To evaluate the effectiveness of our method, we perform extensive experiments on the simulated dataset generated based on a set of real-world web logs. The evaluation results show that conversion predictions by our approach are consistently more accurate and robust than those by existing baselines in dynamic market environment." @default.
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• W2585519298 date "2017-02-02" @default.
• W2585519298 modified "2023-09-30" @default.
• W2585519298 title "Predicting Online Purchase Conversion for Retargeting" @default.
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• W2585519298 doi "https://doi.org/10.1145/3018661.3018715" @default.
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