I'm exploring options for recommender systems optimized for the insurance industry, which would take into account

i) product holdings

ii) user characteristics (segment, age, affluence, etc.).

I want to stress that

a) there are no product ratings available, thus collaborative filtering is not an option

b) recommended products don't have to be similar to products that have already been purchased, thus item-to-item recommendations are most probably not relevant.

Keep in mind that in insurance you rarely want to recommend similar products to those already purchased ones, as someone with the Car insurance is unlikely to want to buy another Motor product, rather Home or maybe Travel, etc.

That's why I want to base recommendations on similarities between the users based on their purchase history and/or demographics

Ideally, I'd like to be able to implement it in R, if not possible, then in Python. Thanks for help and suggestions!

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account

i) product holdings

ii) user characteristics (segment, age, affluence, etc.).

I want to stress that

a) there are no product ratings available, thus collaborative filtering is not an option

b) recommended products don't have to be similar to products that have already been purchased, thus item-to-item recommendations are most probably not relevant.

Keep in mind that in insurance you rarely want to recommend similar products to those already purchased ones, as someone with the Car insurance is unlikely to want to buy another Motor product, rather Home or maybe Travel, etc.

That's why I want to develop recommendations on similarities between the users based on their purchase history and/or demographics

Ideally, I'd like to be able to implement it in R, if not possible, then in Python. Thanks for help and suggestions!

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account

i) product holdings ii) user characteristics (segment, age, affluence, etc.).

I want to stress that

a) there are no product ratings available, thus collaborative filtering is not an option

b) recommended products don't have to be similar to products that have already been purchased, thus item-to-item recommendations are most probably not relevant.

Ideally, I'd like to be able to implement it in R, if not possible, then in Python. Thanks for help and suggestions!

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account

i) product holdings

ii) user characteristics (segment, age, affluence, etc.).

I want to stress that

a) there are no product ratings available, thus collaborative filtering is not an option

b) recommended products don't have to be similar to products that have already been purchased, thus item-to-item recommendations are most probably not relevant.

Keep in mind that in insurance you rarely want to recommend similar products to those already purchased ones, as someone with the Car insurance is unlikely to want to buy another Motor product, rather Home or maybe Travel, etc.

That's why I want to base recommendations on similarities between the users based on their purchase history and/or demographics

Ideally, I'd like to be able to implement it in R, if not possible, then in Python. Thanks for help and suggestions!