Recommender systems are an essential component of online platforms providing users with personalized experiences. Some recommendation scenarios such as social networks and news are extremely dynamic in nature with user interests changing over time and new items being continuously added due to breaking news and trending events.

Recommender Systems (RecSys) are the engine of the modern internet and the catalyst for human decisions. The goal of a recommender system is to generate relevant recommendations for users from a collection of items or services that might interest them. Building a recommendation system is challenging because it requires multiple stages (item retrieval, filtering, ranking, ordering) to work together seamlessly and efficiently during training and inference. The biggest challenges faced by new practitioners are the lack of understanding around what RecSys look like in the real world and the difficulty in transitioning from the simple Matrix Factorization (MF) to more complex deep learning architectures with multiple input features, neural components and prediction heads.

Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, and an ordering stage. These pipelines need to be carefully deployed as a set, requiring coordination during their development and deployment. Data scientists, ML engineers, and researchers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines.

Newcomers to recommender systems often face challenges related to their lack of understanding of how these systems operate in real life. In most online content related to this topic, the focus is on models and algorithms that score items based on the user’s preferences. However, the recommender model alone does not comprise everything needed for serving optimized recommender systems that meet the company’s business objectives. An industry-standard recommender system involves a number of steps, including data preprocessing, defining and training recommender models, as well as filtering and business logic for serving. In this work, we propose the four-stage recommender system, an industry-wide design pattern we have identified for production recommender systems. The four-stage pipeline includes an item retrieval step that prepares a small subset of relevant items for scoring. The filtering stage then cleans up the subset of items based on business logic such as removing out-of-stock or previously seen items. As for the ranking component, it uses a recommender model to score each item in the presented list based on the preferences of the user. In the final step, the scores are re-ordered to provide a final recommendation list aligned with other business needs or constraints such as diversity. In particular, the presented demo demonstrates how easy it is to build and deploy a four-stage recommender system pipeline using the NVIDIA Merlin open-source framework.

In recent years, several deep learning-based algorithms have been proposed for recommendation systems while its adoption in industry deployments have been steeply growing. In particular, NLP-inspired approaches have been successfully adapted for sequential and session-based recommendation problems, which are important for many domains like e-commerce, news and streaming media. In this regard, this hands-on tutorial will offer to the participants: (I) an introduction on the main concepts and algorithms for session-based recommendation, (II) how to build, train and evaluate a session-based recommendation model based on RNN and Transformer architectures, and (III) how to speed up with GPUs the entire RecSys pipeline which encompasses feature engineering, preprocessing, training, evaluation and inference using NVIDIA Merlin - an open source ecosystem for large-scale deep learning recommender systems.

Much of the recent progress in sequential and session-based recommendation has been driven by improvements in model architecture and pretraining techniques originating in the field of Natural Language Processing. Transformer architectures in particular have facilitated building higher-capacity models and provided data augmentation and training techniques which demonstrably improve the effectiveness of sequential recommendation. But with a thousandfold more research going on in NLP, the application of transformers for recommendation understandably lags behind. To remedy this we introduce Transformers4Rec, an open-source library built upon HuggingFace’s Transformers library with a similar goal of opening up the advances of NLP based Transformers to the recommender system community and making these advancements immediately accessible for the tasks of sequential and session-based recommendation. Like its core dependency, Transformers4Rec is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments.

News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift.

In order to survive, companies depend on their capacity to generate and manage knowledge while promoting alignment among its employees. To tackle this problem, it was developed an enterprise collaboration platform named Smart Canvas, a service whose goal is to leverage companies' knowledge and tear down silos by connecting people, teams, and content. These connections are suggested by a Recommender System, using techniques like Topic Modeling, Content-Based Filtering and Graph traversing. Smart Canvas is a multi-tenant Software as a Service, featuring a scalable cloud-based Recommender System architecture, including tools like Spark and Titan Graph Database, deployed on Google Cloud Platform.