ValueError: The user feature matrix specifies more features than there are estimated feature embeddings: 19400 vs 81728.

I have the following datasets:

Users:
10.000 Rows.
Features: User-Id, name, age, los, ou, gender, skills, language, grade, career interests

Trainings:
Training-Id, training name, main skill

Trainings Taken
User-Id, Training-Id, TrainingTaken
TrainingTaken will be a 10 when the user took the training, otherwise it wont appear in the dataset

The idea is to make a recommneder for trainings :)

I used this helper class for the matrices. https://github.com/Med-ELOMARI/LightFM-Dataset-Helper

``from lightfm_dataset_helper.lightfm_dataset_helper import DatasetHelper`

I defined the feature columns for user and trainings.

items_column = "Training-Id"
user_column = "User-Id"
ratings_column = "TrainingTaken"

items_feature_columns = [
    "training name",
    "main skill"
]

user_features_columns = ["name","age","los","ou", "gender", "skills", "language", "grade", "career interests"]

Then I build the matrices
dataset_helper_instance = DatasetHelper(
    users_dataframe=usersdf,
    items_dataframe=trainingsdf,
    interactions_dataframe=trainingstakendf,
    item_id_column=items_column,
    items_feature_columns=items_feature_columns,
    user_id_column=user_column,
    user_features_columns=user_features_columns,
    interaction_column=ratings_column,
    clean_unknown_interactions=True,
)
dataset_helper_instance.routine()

Then I train:

from lightfm import LightFM
from lightfm.cross_validation import random_train_test_split
(train, test) = random_train_test_split(interactions=dataset_helper_instance.interactions, test_percentage=0.2)

model = LightFM(loss='warp')

model.fit(
    interactions=dataset_helper_instance.interactions,
    sample_weight=dataset_helper_instance.weights,
    item_features=dataset_helper_instance.item_features_list,
    user_features=dataset_helper_instance.user_features_list,
    verbose=True,
    epochs=20,
    num_threads=20,
)

then I try to use the predict

import numpy as np
from lightfm.data import Dataset
#predict existing users
scores = model.predict(user_ids=81727, item_ids=[1])
print(scores)

However I am getting this error:
ValueError: The user feature matrix specifies more features than there are estimated feature embeddings: 19400 vs 81728.

what could be wrong?

Start by inspecting the DatasetHelper-generated interactions, weights, item_features_list, and user_features_list alongside the LightFM model dimensions, then trace the model.predict call for existing users. A useful outcome would be a minimal reproduction that identifies whether the mismatch is in the helper-generated matrices or the prediction inputs, with the relevant documentation or validation updated.