Here’s what researchers have to say

California: A recent study shed light on recommendation algorithms that can make a customer’s online shopping experience faster, easier, and more efficient by recommending complementary products each time the customer adds a product to their cart. .

If the customer bought peanut butter, the algorithm would recommend several brands of jelly to buy next. The research was led by Negin Entezari of the University of California-Riverside.

These algorithms typically worked by matching purchased items to items that other shoppers frequently purchased next to them. If the buyer’s habits, tastes, or interests closely resemble those of previous customers, such recommendations could save time, jog memories, and be a welcome addition to the shopping experience.

But what if the customer buys peanut butter to fill a dog toy or bait a mousetrap? What if the customer prefers honey or bananas with their peanut butter? The recommendation algorithm will offer less helpful suggestions, which will cost the retailer a sale and could annoy the customer.

Research by Entezari, who recently earned a doctorate in computer science at UC Riverside, Instacart collaborators and his doctoral supervisor Vagelis Papalexakis, introduced a methodology called tensor decomposition – used by scientists to find patterns in massive volumes of data – in the world of commerce to recommend complementary products better suited to customer preferences.

Tensors can be represented 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 discover patterns in the data.

“Tensors can be used to represent customer buying behaviors,” Entezari said. “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 modes capture product-to-product interactions by considering co-purchased products in a single transaction. ” For example, three hypothetical buyers — A, B, and C — make the following purchases: A: Buys hot dogs, hot dog buns, Coke, and mustard in a single transaction. B: Makes three separate transactions: Cart 1: Hot dogs and hot dog buns; Basket 2: Coke; Basket 3: Mustard C: Hot dogs, hot dog buns and mustard in one transaction.

For a conventional matrix-based algorithm, customer A is identical to customer B because they purchased the same items. Using the tensor decomposition, however, customer A is more closely related to customer C because their behavior was similar. Both had similar products purchased at the same time in a single transaction, although their purchases differed slightly.

The typical recommendation algorithm made predictions based on the item the customer had just purchased, while the tensor decomposition could make recommendations based on what was already in the user’s entire shopping cart. So if a shopper has dog food and peanut butter in their cart but no bread, a tensor-based recommendation algorithm might suggest a refillable dog chew toy instead of jelly if other users also made this purchase.

“Tensors are multidimensional structures that allow modeling of complex and heterogeneous data,” said Papalexakis, associate professor of computer science and engineering. “Instead of just noticing which products are bought together, there’s a third dimension. These products are bought by what type of user, and the algorithm tries to figure out what types of users create that match.” To test their method, Entezari, Papalexakis and co-authors Haixun Wang, Sharath Rao and Shishir Kumar Prasad, all researchers for Instacart, used a public Instacart dataset to train their algorithm. They found that their method outperformed state-of-the-art methods for predicting customer-specific complementary product recommendations. Although further work is needed, the authors conclude that big data tensor decomposition could find its place in large enterprises as well.

“Tensorial methods, while very powerful tools, are still more popular in academic research when it comes to recommender systems,” Papalexakis said. “For industry to adopt them, we need to demonstrate that it’s attractive and relatively easy to substitute for whatever they have that already works.” While previous research has shown the benefits of tensor modeling in recommendation problems, the new publication is the first to do so within the framework of complementary element recommendation, bringing tensor methods closer to industrial adoption and commercialization. technology transfer in the context of recommender systems.

“Tensorial methods have already been successfully adopted by industry, with chemometrics and food quality being prime examples, and each attempt like our work demonstrates the versatility of tensorial methods in being able to tackle such a wide range difficult problems in different areas,” said Papalexakis.

(To receive our daily E-paper on WhatsApp, please Click here. We allow the PDF of the document to be shared on WhatsApp and other social media platforms.)

Posted: Sunday February 6th 2022, 5:57 PM IST

Sharon D. Cole