  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.
