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.

下一个篮子推荐 (NBR) 是基于一系列已经购买的篮子预测下一个物品集的任务，我们提出了一种基于双向 Transformer 篮子推荐模型 (BTBR) 的简单方法，该方法专注于直接建模篮子内部和篮子间的物品之间的关联性，以提高推荐任务的性能。

面向食品购物中下一个新奇购物篮推荐的遮蔽和交换序列建模

Masked and Swapped Sequence Modeling for Next Novel Basket  Recommendation in Grocery Shopping

Recommender systems play an important role in helping people find information
and make decisions in today's increasingly digitalized societies. However, the
wide adoption of such machine learning applications also causes concerns in
terms of data privacy. These concerns are addressed by the recent "General Data
Protection Regulation" (GDPR) in Europe, which requires companies to delete
personal user data upon request when users enforce their "right to be
forgotten". Many researchers argue that this deletion obligation does not only
apply to the data stored in primary data stores such as relational databases
but also requires an update of machine learning models whose training set
included the personal data to delete. We explore this direction in the context
of a sequential recommendation task called Next Basket Recommendation (NBR),
where the goal is to recommend a set of items based on a user's purchase
history. We design efficient algorithms for incrementally and decrementally
updating a state-of-the-art next basket recommendation model in response to
additions and deletions of user baskets and items. Furthermore, we discuss an
efficient, data-parallel implementation of our method in the Spark Structured
Streaming system. We evaluate our implementation on a variety of real-world
datasets, where we investigate the impact of our update techniques on several
ranking metrics and measure the time to perform model updates. Our results show
that our method provides constant update time efficiency with respect to an
additional user basket in the incremental case, and linear efficiency in the
decremental case where we delete existing baskets. With modest computational
resources, we are able to update models with a latency of around
0.2~milliseconds regardless of the history size in the incremental case, and
less than one millisecond in the decremental case.

该研究探讨了在欧洲实施的 “一般数据保护条例” 下，个人数据的删除不仅适用于主数据存储中的数据还适用于包含个人数据的机器学习模型，并设计了有效的算法，用于在下一个购物篮推荐任务中增量和递减地更新此类模型。