recommender-systems | various recommender systems , for course CS F469 | Recommender System library

Let us assume that the CountVectorizer gives you a matrix C of shape (N, m) where N = number of restaurants and m = number of features (here the count of the words).

Now since you want to add numerical features, say you have k such features. You can simply compute these features for each movie and concatenate them to the matrix C. So for each movie now you will have (m+k) features. The shape of C will now be (N, m+k). You can use pandas to concatenate.

Now you can simply compute the Cosine Similarity using this matrix and that way you are taking into account the text features as well as the numeric features

However, I would strongly suggest you normalize these values, as some of the numeric features might have larger magnitudes which might lead to poor results. Also instead of the CountVectorizer, TFIDF matrix or even word embeddings might give you better results

I modified your code a bit and got a similar result with mxnet's. Here is the code in colab.

1. model. you missed axis=1 in the summation operation.

Embeddings are assumed to be sequential.

The first input of Embedding is the input dimension. So, if the input exceeds the input dimension the value is ignored. Embedding assumes that max value in the input is input dimension -1 (it starts from 0).

https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding?hl=ja

As an example, the following code will generate embeddings only for input [4,3] and will skip the input [7, 8] since input dimension is 5.

I think it is more clear to explain it with tensorflow;

You mention "recommended items" so I'll assume you're talking about calculating precision for a recommender engine, i.e. the number of predictions in the top k that are accurate predictions of the user's future interactions.

The objective of a recommender engine is to model future interactions from past interactions. Such a model is trained on a dataset of interactions such that the last interaction is the target and n past interactions are the features.

The precision would therefore be calculated by running the model on a test set where the ground truth (last interaction) was known, and dividing the number of predictions where the ground truth was within the top k predictions by the total number of test items.

Items that the user has not interacted with do not come up because we are training the model on behaviour of other users.