Personalize your search results with Amazon Personalize and Amazon OpenSearch Service integration

Amazon Personalize has launched a new integration with Amazon OpenSearch Service that enables you to personalize search results for each user and assists in predicting their search needs. The Amazon Personalize Search Ranking plugin within OpenSearch Service allows you to improve the end-user engagement and conversion from your website and app search by taking advantage of the deep learning capabilities offered by Amazon Personalize. This feature is also available with self-managed OpenSearch.

Search is crucial in engaging users because it brings high-intent traffic from individuals seeking specific products or categories. Previously, customers found it challenging to capitalize on this traffic and provide relevant search results to their users due to infrastructure limitations or lack of ML expertise. This led to increased instances of users failing to find the items they were searching for. With the Amazon Personalize Search Ranking plugin, customers of OpenSearch Service version 2.9.0 or later can go beyond the traditional keyword matching approach and boost relevant items in an individual user’s search results based on their interests, context, and past interactions in real time. You can also fine-tune the level of personalization for every search query to ensure flexibility and control over the search experience.

AWS Partners like Cognizant are excited by the personalization possibilities that the Amazon Personalize Search Ranking plugin will unlock for their media and retail customers.

“Amazon Personalize has been proven to be highly impactful for many businesses with its cost-effective and streamlined implementation. With the release of the new Amazon Personalize Search Ranking plugin within Amazon OpenSearch Service, we can now rapidly deploy and implement real-time user personalization to search results. We are highly confident that it will deliver improved customer experience and satisfaction as well as increase conversion and clickthrough rates by two to three times. Personalized search is a differentiator, especially for media and retail platforms. We are really excited to be a launch partner with AWS on this release and are looking forward to helping businesses deliver personalized search solutions powered by Amazon Personalize.”

– Andy Huang, Head of AI/ML at Cognizant Servian.

In this post, we show you how search results get personalized based on the user and how they vary when you adjust the personalization weight. We specify a value closer to zero to place less emphasis on personalization, and specify a value closer to 1 to re-rank search results based on a higher level of personalization.

Example use cases

To explore the impact of this new feature in greater detail, let’s review an example using a dataset from the Retail Demo Store.

First, we use OpenSearch Service to get search results for the search query “Grooming.” When the personalization weight is set to 0.0, no personalization takes place. As shown in the following table, the top five search results from OpenSearch Service show the grooming items with a higher gender affinity towards women (refer to the Gender_Affinity column, where M stands for male and F stands for female).

Let’s suppose that a user with gender M (male) performs a search using the same query for “Grooming.” When the personalization weight is set to 0.3, the items with a gender affinity towards men get a subtle boost in ranking. In this example, Premium Men’s Razor, which was originally ranked number 6 in the previous table by OpenSearch Service, gets boosted to rank 2 in the updated table. Similarly, Razor Brand for Men shows up higher in position (rank 6) despite being the lowest-ranked item in the previous table.

Next, we fine-tune the personalization weight to a value of 0.8 to get more personalized search results for “Grooming.” In the following table, the top four items in the search results are highly suited for men. Premium Men’s Razor and Razor Brand for Men shoot up further in rank. We also see other grooming items such as Minimalistic Razor and Fusion5 Razers for Men surfaced at the top of the search results even though they had a lower ranking in our first query.

For more details on how to implement personalized search with OpenSearch Service, refer to Personalizing search results from OpenSearch.

Conclusion

With the new Amazon Personalize Search Ranking plugin, customers of both self-managed OpenSearch and OpenSearch Service v2.9 and above can boost relevant items in their search results by including signals from each user’s history, context, and preferences. The plugin enables you to exercise greater control over the level of personalization for each user and query type, and improve the overall search experience for your users.

For more details on Amazon Personalize, refer to the Amazon Personalize Developer Guide.

About the Authors

Shreeya Sharma is a Sr. Technical Product Manager working with AWS AI/ML on the Amazon Personalize team. She has a background in computer science engineering, technology consulting, and data analytics

Ketan Kulkarni is a Software Development Engineer with the Amazon Personalize team focused on building AI-powered recommender systems at scale. In his spare time, he enjoys reading and traveling.

Prashant Mishra is a Software Development Engineer on the Amazon Personalize team.

Branislav Kveton is a Principal Scientist at AWS AI Labs. He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near optimal solutions as the number of observations increases.