New research brings a methodology called tensor decomposition — used by scientists to find patterns in massive volumes of data — into the world of online shopping to recommend complementary products more carefully tailored to customer preferences.

These algorithms typically work by associating purchased items with items other shoppers have frequently purchased alongside them. If the shopper’s habits, tastes, or interests closely resemble those of previous customers, such recommendations might save time, jog the memory, and be a welcome addition to the shopping experience.

But what if the shopper is buying peanut butter to stuff a dog toy or bait a mousetrap? What if the shopper prefers honey or bananas with their peanut butter? The recommendation algorithm will offer less useful suggestions, costing the retailer a sale and potentially annoying the customer.

New research led by Negin Entezari, who recently received a doctoral degree in computer science at UC Riverside, Instacart collaborators, and her doctoral advisor Vagelis Papalexakis, brings a methodology called tensor decomposition — used by scientists to find patterns in massive volumes of data — into the world of commerce to recommend complementary products more carefully tailored to customer preferences.

Tensors can be pictured as multi-dimensional cubes and are used to model and analyze data with many different components, called multi-aspect data. Data closely related to other data can be connected in a cube arrangement and related to other cubes to uncover patterns in the data.

«Tensors can be used to represent customers’ shopping behaviors,» said Entezari. «Each mode of a 3-mode tensor can capture one aspect of a transaction. Customers form one mode of the tensor and the second and third mode captures product-to-product interactions by considering products co-purchased in a single transaction.»

For example, three hypothetical shoppers — A, B, and C — make the following purchases:

Story Source: Materials provided by University of California — Riverside. Original written by Holly Ober. Note: Content may be edited for style and length.