“Others You May Like” and “Recommended For You” are highly personalized recommendations powered by Transformer-based sequence modeling. “Others You May Like” recommendation requires an anchor item and is usually used on product detail pages, while “Recommended For You” can be used on pages without anchor items, such as home pages and category pages. They take into account both the content and order of a user’s historical clicks and purchases, correctly identify their true intentions, and precisely predict the next items that they would love to check out or purchase. For example, if a user browses a variety of maxi dresses and switches to fashionable shoes, “Others You May Like” model may recommend a mix of scandals and heels that go well with maxi dresses for a casual look during the day or a dressier look at night.
“Similar Items” is a new model type we’ve recently developed and is currently in preview. The “Similar Items” model will use only the product catalog, and not require user events in an effort to accelerate time to test model and preview recommendations results in the console for customers. We’ve leveraged self-supervised learning to accurately capture item similarities based on the metadata such as titles and categories, even if there is no user event ingested.
Machine learning models are created to optimize for a particular objective, which determines how the model is built. “Others You May Like” and “Recommended for You” recommendation models have click-through rate (CTR) as the default optimization objective. Optimizing for CTR emphasizes engagement, and you should optimize for CTR when you want to maximize the likelihood that the user interacts with the recommendation. In contrast, revenue per order is the default optimization objective for the “Frequently Bought Together” recommendation model type, as “Frequently Bought Together” focuses on cross-selling and increasing order values.
For “Others You May Like” and “Recommended for You” recommendation models, we also support conversion rate (CVR) as the objective. Optimizing for conversion rate maximizes the likelihood that the user adds the recommended item to their cart. When CVR is specified as the objective for a customer with sparse add-to-cart events, the multi-task learning mechanism will be automatically activated, and transfer learn from detail-page-view events, which are typically much denser than add-to-cart events.
User event data requirements
Before you create a new model, you must have met the requirements for creating a new model. The type of user events you import, and the amount of data you need, depends on your recommendation (model) type and your optimization objective. For example, you need to meet the following data requirement to train an “Others You May Like” model optimizing for click-through rate:
When you reach the minimum data requirement, you can begin model training. You can import historical user event data to meet the minimum event data requirements faster, or wait until the user event data collection meets the minimum requirements.
Other features for better performance
Our models also support massive catalogs of tens of millions of items and ensure that your customers have the opportunity to discover the entire breadth of your catalog through personalized recommendations. Models are re-trained daily to draw insights from changing catalogs, user behavior, or shopping trends and incorporate them into the recommendations being served. We also correct for bias with extremely popular or on-sale items and better handle long-tail items with sparse data as well as seasonal items to ultimately drive better CTR, CVR and revenue lift for our customers.
The “Ready to Query” column on the Models page will change to “Yes” once the models are finished with the initial training and tuning. Once your models are trained and ready to query, you can preview the results in the cloud console and make prediction requests with the API. Requests aren’t made to a model directly, a predict request is made to a specific Serving Config (previously called a Placement). A Serving Config contains some additional options to use – so you can have multiple placements that call the same model, each with different options like price reranking or diversification settings.
Creating a Serving Config: