Seasonality-Adjusted Conceptual-Relevancy-Aware Recommender System in Online Groceries
Luyi Ma, Jason H.D. Cho, Sushant Kumar, Kannan Achan · 2019
Conceptual relevancy - defined as how well a pair of products are related to each other - plays a significant role in online-grocery shopping behavior. When a customer goes grocery shopping, they first come up with a list of ingredients for recipes they may be interested in. A typical shopping list contains categories of products, many of them conceptually relevant to each other. However, the said user may be flexible on which exact product to purchase. For instance, a user may put `milk,' and `cheese' in their shopping list, but these may not refer to specific products such as `Great value 2% milk,' or `Kraft Singles American Slices.' Modern recommender systems, however, focus much more on how to identify specific items to recommend to customers, rather than the categories that may be relevant. Such an approach may lead the system to occasionally recommend outlier, or noise, ultimately violating conceptual relevancy. Moreover, many recommender systems ignore seasonal components; they assume that customers' shopping behavior is independent of the time of the year. However, conceptual relevancy between two products shifts over time. For instance, if a user is shopping for groceries in the middle of the winter, recommending particular products (say, barbecue-related products) may not be the best strategy even if the contextual (user's past interests, or item that the user is currently viewing) may suggest otherwise. In this paper, we introduce a novel strategy to enforce conceptual relevancy in online-grocery domain. Furthermore, recognizing that conceptual relevancy is heavily influenced by the time of the year, we propose a Bayesian-based seasonality algorithm to capture the drift in conceptual relevancy over time without having to re-train the whole model. The algorithm can be based on any of the popular approaches in recommender systems - either based on matrix factorization or that on neural networks. Through our experiments, we show that our seasonality framework can capture drifts in conceptual relevancy.