Advanced recommendations use machine learning algorithms and neural networks to discover accurate recommendations. These recommendations are dynamic and adapt to user interaction patterns over time and update almost in real time.
The recommendation results are sorted in relevant order which means the first recommended item is most relevant. These recommendations can be further enlisted in the following categories:
Recommended Items
This model generates user-personalized recommendations that cater to individual preferences. The recommendation is generated by considering the engagement patterns of similar users and session-based pattern shifts.
Example Use Cases
- Provide users with the next best items.
- Formulate the 'You May Like', 'You May Be Interested', or 'Recommended For You' collections.
Similar Items
This model generates recommendations for items similar to the one last interacted with. The recommendations are based on factors such as item attributes and historical co-occurrences. By suggesting similar items, this model can help users discover new and relevant products or services.
Example Use Cases
- Provide users with alternative options for products or services they are interested in.
- Help users explore a wider range of products or services that match their interests.
Frequently Viewed Together Items
This model generates recommendations for items that are frequently viewed together with the one last interacted with. By analyzing user behavior and identifying patterns in item co-occurrence, the model can suggest items that the user may be interested in.
Example Use Cases
- Provide users with relevant product bundles or packages.
- Increase sales and engagement by suggesting additional related products or services.
Frequently Bought Together Items
This model generates recommendations for items that are frequently purchased together with the item being viewed or purchased. By analyzing user behavior and identifying historical co-occurring purchase patterns, the model can suggest items that the user may be interested in and complement their current purchase.
Example Use Cases
- Suggest complementary products or services to increase sales and engagement.
- Improve customer satisfaction by anticipating their needs and providing relevant suggestions to complete their purchase.
Popular Items
These are items that are currently trending or popular among users. By analyzing holistic user behavior and identifying patterns in item popularity, the model can suggest items that are in high demand.
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Note These recommendations are stitched as the default fallback for all other AI recommendations and are served if a user has not engaged with any items in the last 2 months. |
Example Use Cases
- Activate new users by exhibiting trends in your platform.
- Help discover new and popular products or services that they may be interested in.