Amazon Personalize- Exploration weight impact on recommendations

Question

According to the documentation, with exploration weight as '0', there should be no exploration and the items returned should be based on relevance. With our testing we don't have the items returned completely based on relevance, it still returns popular items which the user has not interacted with at all. Our dataset has 50 users (with 100 interactions each) and also some 17 anonymous interactions (with no user_id and only session_id).

Is this an issue with the dataset somehow?

This is our user-item interaction schema example:

```{
	"type": "record",
	"name": "Interactions",
	"namespace": "com.amazonaws.personalize.schema",
	"fields": [
		{
			"name": "USER_ID",
			"type": "string"
		},
		{
			"name": "ITEM_ID",
			"type": "string"
		},
		{
			"name": "EVENT_TYPE",
			"type": "string"
		},
		{
			"name": "DEVICE_TYPE",
			"type": "string"
		},
		{
			"name": "LOCATION",
			"type": "string"
		},
		{
			"name": "SESSION_ID",
			"type": [
				"null",
				"string"
			]
		},
		{
			"name": "TIMESTAMP",
			"type": "long"
		}
	]
}

Answer

Hi there,

I believe there is always some innate error in the relevance but in this [blog post](https://aws.amazon.com/blogs/machine-learning/increasing-the-relevance-of-your-amazon-personalize-recommendations-by-leveraging-contextual-information/) are some ways to improve the relevance by leveraging contextual information.

Hope this helps make the recommendations more accurate and [here](https://www.quora.com/What-is-the-best-way-to-test-the-relevance-of-a-recommendation-engine) is an additional resource to help you think about how you'd define/determine relevance.

Regards NN

Amazon Personalize- Exploration weight impact on recommendations

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