Sherpa AI Recommended Items


A Recommendations engine is a very powerful personalization tool to ease out the product discovery process of your customers. It should come as no surprise that the user engagement and conversion rate in a recommendation-based campaign is much higher than in generic campaigns. 69% of business buyers expect Amazon-like buying experiences – such as personalized recommendations.

Sherpa AI ‘Recommended items’ are the intelligent grouping of different items empowered with AI. These recommended items can be used to personalize your marketing campaigns.


  • User item interactions / Events (e.g. Add to Cart, Purchase, Product viewed, etc.)
  • Item Catalog

Key Features

The recommendations are generated from the items in a catalog. You will need at least one type of user-item interaction event for recommendations to be developed. You get to define as many such events as you want and can influence the algorithm.



Product Discovery:

Recommendation automates product discovery for your users. The Sherpa recommendation engine is exceptional to learn the behavior of users and recommend relevant items for each of them. 

Let us think of a catalog having 10 items and N users. For some users, item5 will be more relevant whereas for others item7 will be more relevant. You can expect the recommendations to be served in the following manner.



No code AI platform:

Developing a recommendation system from scratch is very hassling. It requires lots of time, effort, and moreover, expertise. But don’t worry, MoEngage’s Sherpa AI allows you to set up the recommendation engine with no-code and a minimal set of configurations merely in 24 hours.

All you need is to define an item catalog and a set of relevant events from the Sherpa user interface. You can always edit the recommendation settings and experiment around with a different set of events.

With your instructions, Sherpa handles all the data engineering and complex algorithmic tasks in the backend without your further interventions.


Deep Learning Algorithm:

Sherpa AI leverages state-of-art deep learning algorithms to learn user behavior and provide the most relevant results. The algorithm accounts for users' behavior, and respective session-based pattern shifts.

The recommendations are generated even if no interactions are done by a user, also known as the ‘user cold starts problem’. Such users are recommended the popular items initially and then quickly personalize recommendations right after a few interactions are done by the user.

There could also be an ‘items cold starts’ problem. These are the items that were not interacted with by users e.g. new items. The AI resolves this by promoting cold (or not interacted) items often, collecting feedback, and quickly making informed recommendations.


Real-time recommendations:

User intent can change quickly. Something a user was interested in yesterday may not be of relevance to them today. There is a need for recommendation systems that are personalized hotfoot with the rapid shifts in user behavior.

The Sherpa AI  is able to learn quickly from a user’s real-time interest and keeps the recommendations fresh and relevant. These recommendations are updated as soon as in hours.



Was this article helpful?
0 out of 0 found this helpful