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To address this challenge, content-based filtering CBF techniques have evolved that leverage user and item features.

This makes recommender systems less sensitive to missing collaborative signals. But inferring user preferences towards item features still requires them as we will explore later.

In general, content knowledge on users and items provides relief and leverage, but has still some dependencies. Nevertheless, combining collaborative and content-based information can be even more powerful.

We call recommenders that combine different techniques Hybrid Recommenders. So we know the basic vocabulary of recommender systems, but as this is about deep learning, we need to go deeper.

Using deep learning to improve vehicle suggestions, we have two basic goals:. Given a user u and an item i , we calculate a score that serves as a proxy for preference.

This means that high scores mirror high preference and vice versa. This score allows us to rank a set of items according to their relevance for a given user.

Thus, we aim to predict user-item interaction probabilities. To compute this probability, we use a deep neural network with a single output unit.

This unit uses the sigmoid function for activation, which yields output values in the interval 0, 1. Thus, we can interpret the network output as probability and use it for ranking.

The network is then trained on distinguishing between preference and disregard. Therefore, we label all positive user-item combinations with 1 and negatives with 0.

As a result, the learning task presents itself as a binary classification task. Excelling at this task can enhance the relevance of our recommendations.

With a naive approach, we compute these probabilities for all possible user-item combinations. Based on that, we would select the k topmost items and present them to the user as personalized recommendations.

Due to the large number of users and items in recommendation scenarios this is practically infeasible. We need to find a computationally cheap and fast way to narrow down the corpus of items to candidate sets for each user which still contain items that are likely to be relevant.

Therefore, we augment our deep learning approach with approximate nearest neighbor search. We execute this search on dense representations for users and items which requires us to create them in a first step.

Thereafter, approximate nearest neighbor search can find good candidates fast and help us to scale well.

Traditional techniques such as CF or CBF calculate these scores using linear techniques which fail to anticipate underlying nonlinear patterns.

To capture these nonlinearities, we build learning models with higher complexity. Ultimately, we want to achieve the same thing, but just change the underlying model.

Our model combines approximate nearest neighbor search for candidate generation with binary classification for ranking.

User and item representations collection of feature values are provided as real-valued, high-dimensional, and highly sparse vectors.

Starting with them, we need to solve the following tasks:. We solve all these tasks within our overall model. But this is just one part of our approach, the other is how we handle our data.

So we need to get our data in line first before continuing with the model. In a first step, we select features that users and items have in common.

We split these into continuous features such as vehicle price or mileage , and categorical ones such as color and vehicle type. We further define time periods for training and testing our model.

Now, we fetch all events that occurred within these time frames and the respective item features valid at the time. By doing so, we can concentrate on the events and associated items for each individual user.

This lets us determine user preferences as an aggregation of the associated item features. By calculating the mean and standard deviation of all vehicles a user viewed, we get some insights on the preferred price range, but also how the user trades off between price and mileage.

We can build these user representations using a Bayesian approach and thus craft the same features for users as we have for vehicles. Even though they relate to the same concepts, user representations are stochastic, whereas vehicle representations are deterministic.

For instance, the figure below shows a profile, where the user viewed five vehicles. Two of them were black, two were grey, and one was red. This reveals a preference for restrained colors that is reflected by the probability distribution inferred and now part of its profile on the right.

For a continuous feature like price, we can do the same. Whereas a vehicle can be either black, white, or grey and has a single price.

As we have users and items set, we need to target the interactions themselves. We simplified this problem choosing a binary classification approach.

Thus, we label all observed interactions with 1 to denote preference. Since we lack negative feedback signals, we artificially generate them from the positives with a technique called negative sampling.

As a result, we get an equal amount of observed positively labeled interactions as we have negatively labeled.

Now, we are set to go deeper. The overall network consists of three subnetworks as you can see in the following figure: These networks are combined and trained jointly.

Afterwards, we split them to present an overall architecture capable of serving the recommendations in production. Thus, they heavily reduce the dimensionality by compressing the information.

Although embeddings lose their human readability, we benefit from them in the latter process, as they are much more memory-efficient.

Since the representations differ stochastic users vs. With these handy representations for users and items, we begin the process of deriving good items for a given user embedding.

This process is two-fold and comprises the generation of a subset of items from the overall corpus as well as ranking those candidates.

To quickly find candidates that are likely to be relevant for a user, we use approximate nearest neighbor search.

Starting with a user embedding as query, we can efficiently fetch the T closest items for a specific distance metric, e. Acconsenti ai nostri cookie, se continui ad utilizzare questo sito web.

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