model_name
for this model is simple_product_recommender
.features
, i.e. tags
, name
and description
, of your product to define how they relate to each other and the transaction history to learn what users like. It should be used in cases where transaction data is limited (but not non-existent) while the products’ tags
, name
or description
have enough information to be useful.model_name
for this model is product_content_recommender
.model_name
for this model is product_popularity_recommender
.features
together to get the most out of your data. However, it works best with significantly more data than Collaborative Filtering.model_name
for this model is ranking_recommender
.model_name
for this model is simple_user_recommender
.model_name
for this model is simple_product_similarity
.model_name
for this model is simple_user_similarity
. tags
assigned to them. By setting output_type
to Product
, you can get similar products by querying their IDs. Or, you can query using a particular tag
, or set of tags
, to get the most similar products based on those tags.model_name
for this model is product_tag_similarity
.tags
be assigned to products, but doesn’t require any transaction history to work.model_name
for this model is complementary_items
.simple_product_recommender
product_content_recommender
tags
, name
, description
product_factorisation_recommender
product_popularity_recommender
simple_user_recommender
simple_product_similarity
simple_user_similarity
product_tag_similarity
complementary_items
data
array
options
object
options.size
number