In e-commerce, the watchlist enables users to track items over time and has emerged as a primary feature, playing an important role in users' shopping journey. Watchlist items typically have multiple attributes whose values may change over time (e.g., price, quantity). Since many users accumulate dozens of items on their watchlist, and since shopping intents change over time, recommending the top watchlist items in a given context can be valuable. In this work, we study the watchlist functionality in e-commerce and introduce a novel watchlist recommendation task. Our goal is to prioritize which watchlist items the user should pay attention to next by predicting the next items the user will click. We cast this task as a specialized sequential recommendation task and discuss its characteristics. Our proposed recommendation model, Trans2D, is built on top of the Transformer architecture, where we further suggest a novel extended attention mechanism (Attention2D) that allows to learn complex item-item, attribute-attribute and item-attribute patterns from sequential-data with multiple item attributes. Using a large-scale watchlist dataset from eBay, we evaluate our proposed model, where we demonstrate its superiority compared to multiple state-of-the-art baselines, many of which are adapted for this task.
|Title of host publication||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||10|
|State||Published - 11 Feb 2022|
|Event||15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States|
Duration: 21 Feb 2022 → 25 Feb 2022
|Name||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Conference||15th ACM International Conference on Web Search and Data Mining, WSDM 2022|
|Period||21/02/22 → 25/02/22|
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