Masked and Swapped Sequence Modeling for Next Novel Basket
  Recommendation in Grocery Shopping
Ming Li Mozhdeh Ariannezhad Andrew Yates Maarten de Rijke
Next basket recommendation (NBR) is the task of predicting the next set of
items based on a sequence of already purchased baskets. It is a recommendation
task that has been widely studied, especially in the context of grocery
shopping. In next basket recommendation (NBR), it is useful to distinguish
between repeat items, i.e., items that a user has consumed before, and explore
items, i.e., items that a user has not consumed before. Most NBR work either
ignores this distinction or focuses on repeat items. We formulate the next
novel basket recommendation (NNBR) task, i.e., the task of recommending a
basket that only consists of novel items, which is valuable for both real-world
application and NBR evaluation. We evaluate how existing NBR methods perform on
the NNBR task and find that, so far, limited progress has been made w.r.t. the
NNBR task. To address the NNBR task, we propose a simple bi-directional
transformer basket recommendation model (BTBR), which is focused on directly
modeling item-to-item correlations within and across baskets instead of
learning complex basket representations. To properly train BTBR, we propose and
investigate several masking strategies and training objectives: (i) item-level
random masking, (ii) item-level select masking, (iii) basket-level all masking,
(iv) basket-level explore masking, and (v) joint masking. In addition, an
item-basket swapping strategy is proposed to enrich the item interactions
within the same baskets. We conduct extensive experiments on three open
datasets with various characteristics. The results demonstrate the
effectiveness of BTBR and our masking and swapping strategies for the NNBR
task. BTBR with a properly selected masking and swapping strategy can
substantially improve NNBR performance.