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I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage frequency or hours. This information may be sparse (e.g. the user may not have provided me with his age).

Can you suggest an approach that would allow for it?

###My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) item-based collaborative filtering.

To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data:

1. Normalize scale to be compliant with the rating scale.
2. Add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage frequency or hours. This information may be sparse (e.g. the user may not have provided me with his age).

Can you suggest an approach that would allow for it?

My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) item-based collaborative filtering.

To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data:

1. Normalize scale to be compliant with the rating scale.
2. Add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage frequency or hours). This information may be sparse (e.g. the user may not have provided me with his age).

Can you suggest an approach that would allow for it?

###My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) user-based collaborative filtering.

To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data:

1. Normalize scale to be compliant with the rating scale.
2. Add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage frequency or hours. This information may be sparse (e.g. the user may not have provided me with his age).

Can you suggest an approach that would allow for it?

###My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) item-based collaborative filtering.

To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data:

1. Normalize scale to be compliant with the rating scale.
2. Add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings, but also on behavioural and demographical variables like sex, age, location, service usage frequency or hours). This information may be sparse (e.g. user may not have provided me with his age). Can you suggest an approach that would allow for it?

My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) user-based collaborative filtering. To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data: (1) normalize scale to be compliant with ratings scale (2) add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.

I wanted to enhance a recommendation engine with information relying not only on past purchases or ratings but also on behavioral and demographical variables like sex, age, location, service usage frequency or hours). This information may be sparse (e.g. the user may not have provided me with his age). 

Can you suggest an approach that would allow for it?

###My first approach:

After screening the general approaches to recommender systems, I think the one that is able to implement my idea is custom(?) user-based collaborative filtering. 

To be more precise: I would include user profile information in the same way item ratings are introduced, probably with two alterations to raw data:

1. Normalize scale to be compliant with the rating scale.
2. Add a parameter in my algorithm that would put a weight (e.g. 20% or 50%) on user profile rows, as there may be 10 user-profile variables and a million product items.