Steve Zegalia

New in Atlas Search: Improve Content Recommendations With “More Like This”

We’re proud to announce the release of More Like This, a key MongoDB Atlas Search feature that allows developers to easily build more relevant and engaging experiences for their end users. With the moreLikeThis operator, you can display documents that are similar to a result document. In this article, we’ll explain how it works and how you can get started using this new feature. Content recommendation done easily People who use travel booking apps, streaming services, and e-commerce websites are likely familiar with “Frequently Bought With,” “Similar Products,” or “You Might Also Enjoy” sections in their search experiences — in other words, content recommendation that guides them toward new or related products to buy, movies to stream, recipes to make, or news articles to read (among other things). Instead of building and tuning a recommendation engine to provide this functionality, developers can create engaging, browsable search experiences by defining a similarity threshold between documents to surface relevant documents. How it works Under the hood, the moreLikeThis search operator extracts the most representative terms from a reference document or documents and returns a set of similar documents. The representative terms are selected based on term frequency-inverse document frequency (TF-IDF), which is calculated by looking at a given term’s frequency in a given document multiplied by its frequency in the corpus. TF-IDF is calculated by looking at a term’s frequency multiplied by its frequency in the corpus. Atlas Search indexes term frequency by default, which means there is less up-front configuration required when compared with other search solutions. Additionally, developers have the ability to define what constitutes sufficient similarity for their use cases, with control over variables such as the number of query terms selected and the minimum and maximum document frequency thresholds. Use cases An example use case might look like this: An online bookstore wants to upsell users who have reached the checkout stage with similar books. On the checkout page, the user is served with a More Like This query result in the form of an “Other Books You Might Like” section that contains an array of book titles based on multiple fields in the document (e.g., title, publisher, genre, author). More Like This can be applied to use cases like ecommerce, content management systems, application search, or anywhere you want to share more relevant content with your users to drive deeper engagement. For more examples of how to configure More Like This, refer to our examples in the Docs . To learn how to get started with More Like This, refer to our documentation . For real-world Atlas Search implementation examples, go to our Developer Center .