No user feature matrix defined but still get ValueError: The user feature matrix specifies more features than there are estimated feature embeddings

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I've seen a few posts that mentioned this error but I haven't been able to find a solution that resolves my case. I'd really appreciate any tip, advice, or direction as to how to resolve the error. I do not have a user feature matrix for my model, just an item feature matrix however when I try model.predict (on a single user id), I get the error: ValueError: The user feature matrix specifies more features than there are estimated feature embeddings.

I'm using data from Steam's API and this is what I have so far:

>`dataset = Dataset()`
`dataset.fit((x['author.steamid'] for x in get_ratings()), (x['appID'] for x in get_ratings()))`

>`dataset.fit_partial(items=(x['appID'] for x in get_game_features()),
item_features=(x['game_topic'] for x in get_game_features()))`

>`(interactions, weights) = dataset.build_interactions(((x['author.steamid'], x['appID']) for x in get_ratings()))
print(repr(interactions))`

><2873901x74 sparse matrix of type ''
with 3973496 stored elements in COOrdinate format>

>`item_features = dataset.build_item_features(((x['appID'], [x['game_topic']]) for x in get_game_features()))
print(repr(item_features))`

><74x93 sparse matrix of type ''
with 148 stored elements in Compressed Sparse Row format>

>`model = LightFM(loss='warp')`

>`(train, test) = random_train_test_split(interactions=interactions, test_percentage=0.2)`

>`model.fit(train, item_features=item_features, epochs=2)`

And then I try to predict for an individual id and get the error:
>`model.predict(np.int64(76561198360721908),np.arange(interactions.shape[1]),user_features=None,item_features=item_features)`
ValueError Traceback (most recent call last)
in
----> 1 model.predict(np.int64(76561198360721908),np.arange(interactions.shape[1]),user_features=None,item_features=item_features)
~\Anaconda3\lib\site-packages\lightfm\lightfm.py in predict(self, user_ids, item_ids, item_features, user_features, num_threads)
714
715 (user_features,
--> 716 item_features) = self._construct_feature_matrices(n_users,
717 n_items,
718 user_features,
~\Anaconda3\lib\site-packages\lightfm\lightfm.py in _construct_feature_matrices(self, n_users, n_items, user_features, item_features)
303 if self.user_embeddings is not None:
304 if not self.user_embeddings.shape[0] >= user_features.shape[1]:
--> 305 raise ValueError('The user feature matrix specifies more '
306 'features than there are estimated '
307 'feature embeddings: {} vs {}.'.format(

ValueError: The user feature matrix specifies more features than there are estimated feature embeddings: 2873901 vs 400456181.

Thanks in advance!

Start with the reported call to model.predict and the _construct_feature_matrices path in lightfm.py, then compare the shapes of user_features, item_features, and the model's estimated embeddings. Done means the individual prediction no longer raises the reported ValueError with the supplied interaction and item-feature matrices.

Written by the indexing model from the issue text.