Building a Recommender System, Part 2

This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.

By Matthew Mahowald, Open Data Group

In a previous post, we looked at neighborhood-based methods for building recommender systems. This post explores an alternative technique for collaborative filtering using latent factor models. The technique we’ll use naturally generalizes to deep learning approaches (such as autoencoders), so we’ll also implement our approach using Tensorflow and Keras.

Cinema doors


The Dataset

We’ll re-use the same MovieLens dataset for this post that we worked on last time for our collaborative filtering model. GroupLens has made the dataset available here.

First, let’s load in this data:

Let’s take a quick look at the top 20 most-viewed files:

title genre
2858 American Beauty (1999) Comedy|Drama
260 Star Wars: Episode IV - A New Hope (1977) Action|Adventure|Fantasy|Sci-Fi
1196 Star Wars: Episode V - The Empire Strikes Back… Action|Adventure|Drama|Sci-Fi|War
1210 Star Wars: Episode VI - Return of the Jedi (1983) Action|Adventure|Romance|Sci-Fi|War
480 Jurassic Park (1993) Action|Adventure|Sci-Fi
2028 Saving Private Ryan (1998) Action|Drama|War
589 Terminator 2: Judgment Day (1991) Action|Sci-Fi|Thriller
2571 Matrix, The (1999) Action|Sci-Fi|Thriller
1270 Back to the Future (1985) Comedy|Sci-Fi
593 Silence of the Lambs, The (1991) Drama|Thriller
1580 Men in Black (1997) Action|Adventure|Comedy|Sci-Fi
1198 Raiders of the Lost Ark (1981) Action|Adventure
608 Fargo (1996) Crime|Drama|Thriller
2762 Sixth Sense, The (1999) Thriller
110 Braveheart (1995) Action|Drama|War
2396 Shakespeare in Love (1998) Comedy|Romance
1197 Princess Bride, The (1987) Action|Adventure|Comedy|Romance
527 Schindler’s List (1993) Drama|War
1617 L.A. Confidential (1997) Crime|Film-Noir|Mystery|Thriller
1265 Groundhog Day (1993) Comedy|Romance



Collaborative filtering models typically work best when each item has a decent number of ratings. Let’s restrict to only the 500 most popular films (as determined by number of ratings). We’ll also reindex by movieid and userid:

Next, as mentioned in the previous post, we should normalize our rating data. We create an adjusted rating by subtracting off the overall mean rating, the mean rating for each item, and then the mean rating for each user.

This produces a “preference rating” \tilde{r}_{u,i} defined by

\tilde{r}_{u,i} := r_{u,i} - \bar{r} - \bar{r}_{i} - \bar{r}_{u}

The intuition for \tilde{r} is that \tilde{r} = 0 means that user u’s rating for item i is exactly what we would guess if all we knew was the average overall ratings, item ratings, and user ratings. Any values above or below 0 indicate deviations in preference from this baseline. To distinguish \tilde{r} from the raw rating r, I’ll refer to the former as the user’s preference for item i and the latter as the user’s rating of item i.

Let’s build the preference data using ratings for the 500 most popular films:

The output of this block of code is two objects: prefs, which is a dataframe of preferences indexed by movieid and userid; and pref_matrix, which is a matrix whose (i,j)th entry corresponds to the rating user i gives movie j (i.e. the columns are movies and each row is a user). In cases where the user hasn’t rated the item, this matrix will have a NaN.

The maximum and minimum preferences in this data are 3.923 and -4.643, respectively. Next, we’ll build an actual model.


Latent-factor collaborative filtering

At this stage, we’ve constructed a matrix P (called pref_matrix in the Python code above). The idea behind latent-factor collaborative filtering models is that each user’s preferences can be predicted by a small number of latent factors (usually much smaller than the overall number of items available):

\tilde{r}_{u,i} \approx f_{i}(\lambda_{1}(u), \lambda_{2}(u), \ldots, \lambda_{n}(u))

Latent factor models thus require answering two related questions:

  1. For a given user u, what are the corresponding latent factors \lambda_{k}(u)?
  2. For a given collection of latent factors, what is the function f_{i}, i.e., what is the relationship between the latent factors and a user’s preferences for each item?

One approach to this problem is to attempt to solve for both the f_{i}’s and \lambda_{k}’s by making the simplifying assumption that each of these functions is linear:

\lambda_{k}(u) = \sum_{i} a_{i} \tilde{r}_{u,i}

f_{i} = \sum_{k} b_{k} \lambda_{k}

Taken over all items and users, this can be re-written as a linear algebra problem problem: find matrices F and \Lambda such that

 P \approx F  \Lambda  P,

where P is the matrix of preferences, \Lambda is the linear transformation that projects a user’s preferences onto latent variable space, and F is the linear transformation that reconstructs the user’s ratings from that user’s representation in latent variable space.

The product F \Lambda will be a square matrix. However, by choosing a number of latent variables strictly less than the number of items, this product will necessarily not be full rank. In essence, we are solving for F and \Lambda such that the product F \Lambda best approximates the identity transformation on the preferences matrix P. Our intuition (and hope) is that this will reconstruct accurate preferences for each user. (We will tune our loss function to ensure that this is in fact the case.)


Model implementation

As advertised, we’ll be building our model in Keras + Tensorflow so that we’re well-positioned for any future generalization to deep learning approaches. This is also a natural approach to the type of problem we’re solving: the expression

P \approx F \Lambda P

can be thought of as describing a two-layer dense neural network whose layers are defined by F and \Lambda and whose activation function is just the identity map (i.e. the function \sigma(x) = x).

First, let’s import the packages we’ll need and set the encoding dimension (the number of latent variables) we want for this model.

Next, define the model itself as a composition of an “encoding” layer (projection onto latent variable space) and a “decoding” layer (recovery of preferences from latent variable representation). The recommender model itself is just a composition of these two layers.


Custom loss functions

At this point, we could train our model directly to just reproduce its inputs (this is essentially a very simple autoencoder). However, we’re actually interested in picking F and \Lambda that correctly fill in missing values. We can do this through a careful application of masking and a custom loss function.

Recall that prefs_matrix currently consists largely of NaNs—in fact, there’s only one zero value in the whole dataset:

In prefs_matrix, we can fill any missing values with zeros. This is a reasonable choice because we’ve already performed some normalization of the ratings, so 0 represents our naive guess for a user’s preference for a given item. Then, to create training data, use prefs_matrix as the target and selectively mask nonzero elements in prefs_matrix to create the input (“forgetting” that particular user-item preference). We can then build a loss function which strongly penalizes incorrectly guessing the “forgotten” values, i.e., one which is trained to construct novel ratings from known ratings. Here’s our function:

def lambda_mse(frac=0.8):
    Specialized loss function for recommender model.

    :param frac: Proportion of weight to give to novel ratings.
    :return: A loss function for use in a Lambda layer.
    def lossfunc(xarray):
        x_in, y_true, y_pred = xarray
        zeros = tf.zeros_like(y_true)

        novel_mask = tf.not_equal(x_in, y_true)
        known_mask = tf.not_equal(x_in, zeros)

        y_true_1 = tf.boolean_mask(y_true, novel_mask)
        y_pred_1 = tf.boolean_mask(y_pred, novel_mask)

        y_true_2 = tf.boolean_mask(y_true, known_mask)
        y_pred_2 = tf.boolean_mask(y_pred, known_mask)

        unknown_loss = losses.mean_squared_error(y_true_1, y_pred_1)
        known_loss = losses.mean_squared_error(y_true_2, y_pred_2)

        # remove nans
        unknown_loss = tf.where(tf.is_nan(unknown_loss), 0.0, unknown_loss)

        return frac*unknown_loss + (1.0 - frac)*known_loss
    return lossfunc

By default, the loss this returns is a 20%-80% weighted sum of the overall MSE and the MSE of just the missing ratings. This loss function requires the input (with missing preferences), the predicted preferences, and the true preferences.

At least as of the date of this post, Keras and TensorFlow don’t currently support custom loss functions with three inputs (other frameworks, such as PyTorch, do). We can get around this fact by introducing a “dummy” loss function and a simple wrapper model. Loss functions in Keras require only two inputs, so this dummy function will ignore the “true” values.

Next, our wrapper model. The idea here is to use a lambda layer (‘loss’) to apply our custom loss function ('lambda_mse'), and then use our custom loss function for the actual optimization. Using Keras’s functional API makes it very easy to wrap the recommender we already defined with this simple wrapper model.



To generate training data for our model, we’ll start with the preferences matrix pref_matrix and randomly mask (i.e. set to 0) a certain fraction of the known ratings for each user. Structuring this as a generator allows us to make an essentially unlimited collection of training data (though in each case, the output is constrained to be drawn from the same fixed set of known ratings). Here’s the generator function:

def generate(pref_matrix, batch_size=64, mask_fraction=0.2):
    Generate training triplets from this dataset.

    :param batch_size: Size of each training data batch.
    :param mask_fraction: Fraction of ratings in training data input to mask. 0.2 = hide 20% of input ratings.
    :param repeat: Steps between shuffles.
    :return: A generator that returns tuples of the form ([X, y], zeros) where X, y, and zeros all have
             shape[0] = batch_size. X, y are training inputs for the recommender.

    def select_and_mask(frac):
        def applier(row):
            row = row.copy()
            idx = np.where(row != 0)[0]
            if len(idx) > 0:
                masked = np.random.choice(idx, size=(int)(frac*len(idx)), replace=False)
                row[masked] = 0
            return row
        return applier

    indices = np.arange(pref_matrix.shape[0])
    batches_per_epoch = int(np.floor(len(indices)/batch_size))
    while True:

        for batch in range(0, batches_per_epoch):
            idx = indices[batch*batch_size:(batch+1)*batch_size]

            y = np.array(pref_matrix[idx,:])
            X = np.apply_along_axis(select_and_mask(frac=mask_fraction), axis=1, arr=y)

            yield [X, y], np.zeros(batch_size)

Let’s check that this generator’s masking functionality is working correctly:

To complete the story, we’ll define a training function that calls this generator and allows us to set a few other parameters (number of epochs, early stopping, etc):

Recall that \Lambda and F are 500 \times 25 and 25 \times 500 dimensional matrices, respectively, so this model has 2 \times 25 \times 500 = 25000 parameters. A good rule of thumb with linear models is to have at least 10 observations per parameter, meaning we’d like to see 250,000 individual user ratings vectors during training. We don’t have nearly enough users for that, though, so for this tutorial, we’ll skimp by quite a bit—let’s settle for a maximum of 12,500 observations (stopping the model earlier if loss doesn’t improve).

The output of this training process (at least on my machine) gives a loss of 0.6321, which means that on average we’re within about 0.7901 units of a user’s true preference when we haven’t seen it before (recall that this loss is 80% from unknown preferences, and 20% from the knowns). Preferences in our data range between -4.64 and 3.92, so this is not too shabby!


Predicting ratings

To generate a prediction with our model, we have to call the recommender model we trained earlier after normalizing the ratings along the various dimensions. Let’s assume that the input to our predict function will be a dataframe indexed by (movieid, userid), and with a single column named "rating".

def predict(ratings, recommender, mean_0, mean_i, movies):
    # add a dummy user that's seen all the movies so when we generate
    # the ratings matrix, it has the appropriate columns
    dummy_user = movies.reset_index()[["movieid"]].copy()
    dummy_user["userid"] = -99999
    dummy_user["rating"] = 0
    dummy_user = dummy_user.set_index(["movieid", "userid"])

    ratings = ratings["rating"]

    ratings = ratings - mean_0
    ratings = ratings - mean_i
    mean_u = ratings.groupby("userid").mean()
    ratings = ratings - mean_u

    ratings = ratings.append(dummy_user["rating"])

    pref_mat = ratings.reset_index()[["userid", "movieid", "rating"]].pivot(index="userid", columns="movieid", values="rating")
    X = pref_mat.fillna(0).values
    y = recommender.predict(X)

    output = pd.DataFrame(y, index=pref_mat.index, columns=pref_mat.columns)
    output = output.iloc[1:] # drop the bad user

    output = output.add(mean_u, axis=0)
    output = output.add(mean_i, axis=1)
    output = output.add(mean_0)

    return output

Let’s test it out! Here’s some sample ratings for a single fake user, who really likes Star Wars and Jurassic Park and doesn’t like much else:

userid 1 title
260 4.008329 Star Wars: Episode IV - A New Hope (1977)
1198 3.942005 Raiders of the Lost Ark (1981)
1196 3.860034 Star Wars: Episode V - The Empire Strikes Back…
1148 3.716259 Wrong Trousers, The (1993)
904 3.683811 Rear Window (1954)
2019 3.654374 Seven Samurai (The Magnificent Seven) (Shichin…
913 3.639756 Maltese Falcon, The (1941)
318 3.637150 Shawshank Redemption, The (1994)
745 3.619762 Close Shave, A (1995)
908 3.608473 North by Northwest (1959)


Interestingly, even though the user gave Star Wars a 5 as input, the model only predicts a rating of 4.08 for Star Wars. But it does recommend the Empire Strikes Back and Raiders of the Lost Ark, which seem like reasonable recommendations for those preferences.

Now let’s reverse this user’s ratings for Star Wars and Jurassic Park, and see how the ratings change:

userid 1 title
2019 3.532214 Seven Samurai (The Magnificent Seven) (Shichin…
50 3.489284 Usual Suspects, The (1995)
2858 3.480124 American Beauty (1999)
745 3.466157 Close Shave, A (1995)
1148 3.415981 Wrong Trousers, The (1993)
1197 3.415527 Princess Bride, The (1987)
527 3.386785 Schindler’s List (1993)
750 3.342154 Dr. Strangelove or: How I Learned to Stop Worr…
1252 3.338330 Chinatown (1974)
1207 3.335204 To Kill a Mockingbird (1962)


Note that Seven Samurai features prominently in both lists. In fact, Seven Samurai has the highest average rating of any film in this dataset (at 4.56), and looking at the top 20 or top 50 recommended films for both users has even more common films showing up that happen to be very highly rated overall.


Conclusions and further reading

The latent factor representation we’ve built can also be thought of as defining an embedding of items into some lower-dimensional space, as opposed to an embedding of users. This lets us do some interesting things—for example, we can compare distances between each item’s vector representation to understand how similar or different two films are. Let’s compare Star Wars against The Empire Strikes Back and American Beauty:

Note that 33 is the column index corresponding to Star Wars (different from its movieid of 260), 144 is the column index corresponding to Empire Strikes Back, and 401 is the column index of American Beauty.

Comparing the distances, we see that with a distance of 0.209578, Star Wars and Empire Strikes Back are much closer in latent factor space than Star Wars and American Beauty are.

With a little bit of further work, it’s also possible to answer other questions in latent factor space like “which film is least similar to Star Wars?”

Variations on this type of technique lead to autoencoder-based recommender systems. For futher reading, there’s also a family of related models known as matrix factorization models, which can incorporate both item and user features as well as the raw ratings.