Wide & Deep Learning for Recommender Systems

“A recommender system can be viewed as a search ranking system, where the input query is a set of user and contextual information, and the output is a ranked list of items. Given a query, the recommendation task is to find the relevant items in a database and then rank the items based on certain objectives, such as clicks or purchases.

One challenge in recommender systems, similar to the general search ranking problem, is to achieve both memorization and generalization. Memorization can be loosely defined as learning the frequent co-occurrence of items or features and exploiting the correlation available in the historical data. Generalization, on the other hand, is based on transitivity of correlation and explores new feature combinations that have never or rarely occurred in the past. Recommendations based on memorization are usually more topical and directly relevant to the items on which users have already performed actions. Compared with memorization, generalization tends to improve the diversity of the recommended items. In this paper, we focus on the apps recommendation problem for the Google Play store, but the approach should apply to generic recommender systems.

For massive-scale online recommendation and ranking systems in an industrial setting, generalized linear models such as logistic regression are widely used because they are simple, scalable and interpretable. The models are often trained on binarized sparse features with one-hot encoding. E.g., the binary feature \user_installed_app=netflix" has value 1 if the user installed Net ix. Memorization can be achieved effectively using cross-product transformations over sparse features, such as AND (user_installed_app=netflix, impres- sion_app=pandora"), whose value is 1 if the user installed Net ix and then is later shown Pandora. This explains how the co-occurrence of a feature pair correlates with the target label. Generalization can be added by using features that are less granular, such as AND (user_installed_category=video, impression_category=music), but manual feature engineering is often required. One limitation of cross-product trans- formations is that they do not generalize to query-item feature pairs that have not appeared in the training data.

Embedding-based models, such as factorization machines [5] or deep neural networks, can generalize to previously un- seen query-item feature pairs by learning a low-dimensional dense embedding vector for each query and item feature, with less burden of feature engineering. However, it is difficult to learn effective low-dimensional representations for queries and items when the underlying query-item matrix is sparse and high-rank, such as users with specific preferences or niche items with a narrow appeal. In such cases, there should be no interactions between most query-item pairs, but dense embeddings will lead to nonzero predictions for all query-item pairs, and thus can over-generalize and make less relevant recommendations. On the other hand, linear models with cross-product feature transformations can memorize these exception rules" with much fewer parameters.”

“An overview of the app recommender system is shown in Figure 2. A query, which can include various user and contextual features, is generated when a user visits the app store. The recommender system returns a list of apps (also referred to as impressions) on which users can perform certain actions such as clicks or purchases. These user actions, along with the queries and impressions, are recorded in the logs as the training data for the learner.

Since there are over a million apps in the database, it is intractable to exhaustively score every app for every query within the serving latency requirements (often O(10) milliseconds). Therefore, the first step upon receiving a query is retrieval. The retrieval system returns a short list of items that best match the query using various signals, usually a combination of machine-learned models and human-defined rules. After reducing the candidate pool, the ranking system ranks all items by their scores. The scores are usually P(yjx), the probability of a user action label y given the features x, including user features (e.g., country, language, demographics), contextual features (e.g., device, hour of the day, day of the week), and impression features (e.g., app age, historical statistics of an app). In this paper, we focus on the ranking model using the Wide & Deep learning framework.”

“Note that there is a distinction be- tween joint training and ensemble. In an ensemble, individual models are trained separately without knowing each other, and their predictions are combined only at inference time but not at training time. In contrast, joint training optimizes all parameters simultaneously by taking both the wide and deep part as well as the weights of their sum into account at training time. There are implications on model size too: For an ensemble, since the training is disjoint, each individual model size usually needs to be larger (e.g., with more features and transformations) to achieve reasonable accuracy for an ensemble to work. In comparison, for joint training the wide part only needs to complement the weak- nesses of the deep part with a small number of cross-product feature transformations, rather than a full-size wide model.

Joint training of a Wide & Deep Model is done by back- propagating the gradients from the output to both the wide and deep part of the model simultaneously using mini-batch stochastic optimization.”