How to create mapping for item_features and build item_features if the feature contains multiple values for each item?

[Ivanclj]

Hi,

I was following the example of Building datasets and tried to build the movielens dataset myself. However, when I tries to build the mapping for item and item features, I realized that the feature I choose, which is the genres in movies.csv, has multiple values in one cell (eg. Action|Fantasy|Comedy) instead of just one value. How should I use dataset.fit_partial to create the mapping in these case? And the same goes for building interactions. Thank you so much!

[maciejkula]

Have you had a look at the docs and the example?

In particular, have a look at the docs for building features to understand what arguments you need to pass in to allow multiple features for each item: http://lyst.github.io/lightfm/docs/lightfm.data.html#lightfm.data.Dataset.build_item_features. To create mappings, you simply pass all possible features in sequentially.

[hammadkhann]

Hey I have a list of tags in a dataframe column against each entity and same goes with categories how do I feed it into the build_item_features function kindly help I am confused?

[maciejkula]

Have you read the documentation? What have you tried that didn't work? If you give me a couple of examples I'll be able to improve the documentation for future users.

[maciejkula]

This doesn't look right! Should we try to figure out constructing the interaction matrix first? Do you want to walk me through what you are trying to do and what you expect to happen?

(Can you also try using Markdown formatting for code blocks? It makes everything much easier to read.)

[hammadkhann]

Can we talk some where else?? Linkedin or somewhere?

[maciejkula]

I think I prefer to do it here.

[hammadkhann]

Ok then lets start with user interaction matrix the idea was to recommend user the brands or entities of particular category in which the user is searching to make interaction matrix I use the user activity data in which if he views some entity details and he have interacted with it similarly if user have wishlist some entity so he likes it. So the above user_interaction dataframe have been created using these two measures.

[hammadkhann]

Is this the right way to make interaction matrix?

[maciejkula]

This makes sense. How do you build a dataset from it?

[hammadkhann]

we have our data on elastic search I did a query for rest call which was responsible for viewing entity details so it gives us the entity id and user id who has viewed it, and wishlist data was queried by user profile data.

[maciejkula]

I mean, how you create the sparse interaction matrix using the Dataset class?

[hammadkhann]

right now I am doing this using entity id and user id from the dataframe I have built I have also attached the snapshot above of named user_interaction dataframe

(interactions, weights) = dataset.build_interactions(((x['user_id'], x['entity_id'])
for index,x in user_interaction.iterrows()))

[maciejkula]

Please use Markdown code blocks.

This looks OK; does the resulting matrix have the shape and density you expect?

[hammadkhann]

I am fitting my data like this:
is it right to take entity ids from entity_data and not from user_interaction? because not all the entities id will be present in the user_interaction data ?
dataset = Dataset() dataset.fit((i['user_id'] for index,i in user_interaction.iterrows()), (i['entity_id'] for index,i in entity_data.iterrows()), item_features = entity_final[feat] )

[hammadkhann]

yes it has same shape and density I expected.

[maciejkula]

Have a look at the Markdown guide to understand how to do code blocks.

I'm sorry, I am not quite sure what your question is.

[hammadkhann]

I am sorry let me see it

[hammadkhann]

dataset = Dataset() 

dataset.fit((i['user_id'] for index,i in user_interaction.iterrows()), (i['entity_id'] for index,i in entity_data.iterrows()), item_features = entity_final[feat] )

Thats how I am fitting my data. Anyways now tell me how can I make my item_feature_matrix? I have attached the dataframe screenshot above name of the dataframe is entity_final.

[maciejkula]

OK. You need to pass an iterable of (entity_id, [features of that entiity]) tuples.

Assuming (for example) your features are in the Tags column, you could something akin to this:

features = [(x['entity_id'], x['Tags'].split(', ')) for x in dataframe.iterrrows()]
dataset.build_item_features(features)

Remember you have to pass the features names to fit before running this so that feature name mappings are constructed first.

[hammadkhann]

Below is the code through which I am making item feature dataset right now

entity_features=list(entity_final.columns)

#deleting entity id from entity features list.
del entity_features[1]

#building item features dataset
item_features = dataset.build_item_features(((x['entity_id'], feat) 
                              for index,x in entity_final.iterrows()))

print(item_features)

[hammadkhann]

Yes I know that thats why I have passed feature names at the time of fitting the dataset

[maciejkula]

Does it work? If not, can you try what I suggested?

[hammadkhann]

It is compiling but not giving me good results and also I am not sure what is happening underneath with these features thanks for your help let me try your thing.

[hammadkhann]

If I have to use multiple features of an entity like with tags I also use entity rank and rating as a feature so how will I pass it?

[maciejkula]

You have to combine all features for a given entity into a single list.

[hammadkhann]

Ok thankyou Its working great now :)