Online Recommendation Definition – A recommender system (or recommender system) is a class of machine learning that uses data to predict, narrow down, and find what people are looking for among an exponentially growing selection.
A recommender system is an artificial intelligence or AI algorithm, often related to machine learning, that uses big data to recommend or recommend other products to consumers. These can be based on a variety of criteria, including past purchases, search history, demographic information and other factors. Recommender systems are very useful because they help users find products and services that they might not be able to find on their own.
Online Recommendation Definition
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Recommender systems are trained to learn about human and product preferences, prior decisions, and characteristics using data collected about their interactions. These include impressions, clicks, likes and purchases. Recommender systems are favored by content and product providers due to their ability to predict consumers’ interests and desires at a highly personalized level. They can guide consumers to any product or service they are interested in, from books to videos to health classes to clothing.
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Although there are a large number of algorithms and recommendation techniques, most fall into the following broad categories: collaborative filtering, content filtering, and contextual filtering.
Collaborative filtering algorithms recommend items (this is the filter part) based on user preference data (this is the collaborative part). This approach exploits the similarity in user preference behavior, and the recommendation algorithm learns to predict future interactions, given previous interactions between users and objects. These recommender systems build models based on past user behavior, such as previously purchased items or ratings on those items, and similar decisions made by other users. The idea is that if certain people have made similar decisions and purchases in the past, such as choosing a movie, they are more likely to agree to other choices in the future. For example, if a collaborative filtering recommender knows that you and another user have similar tastes in movies, it can recommend movies to you that it knows the other user already likes.
Content filters, on the other hand, use attributes or characteristics of items (parts of content) to recommend other items similar to user preferences. This approach models the probability of a new interaction by providing information about the user and the objects they have interacted with (e.g., user’s age, category of restaurant food, average movie review) based on the similarity of object and user characteristics. For example, if a content filtering recommender sees that you like the movies Sleepless in Seattle and You Have Mail, it can recommend another movie of the same genre and/or character, such as Joe Bar against the volcano.
Contextual filtering incorporates user’s contextual information into the recommendation process. Netflix talked at GTC about making better recommendations by developing them as contextual sequence predictions. This approach uses a sequence of contextual user actions plus the current context to predict the likelihood of future actions. In Netflix’s example, given each user’s sequence (country, device, date, and movie viewing time), they trained a model to predict what to watch next.
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Suppose the user has purchased a scarf. Why not recommend a matching hat to complete the look? This feature is often enabled through AI-based algorithms, such as the “Complete Look” or “You May Also Like” sections on e-commerce platforms such as Amazon, Walmart, Target, etc.
AI-based recommendation engines analyze a person’s buying behavior and identify patterns, suggesting content that might match their interests. This is what Google and Facebook actively use when recommending ads, or what Netflix does behind the scenes when recommending movies and TV shows.
As a mass-market product that millions of people consume digitally, banking is critical to referrals. Understanding a customer’s financial situation and their past preferences, along with data from thousands of similar users, is incredibly powerful.
Recommender systems are an essential part of driving personalized user experiences, deeper engagement with customers, and powerful decision support tools in business, entertainment, healthcare, finance, and other industries. On some of the largest exchanges, referrals account for 30% of revenue. A 1% improvement in recommendation quality can translate into billions of dollars in revenue.
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How a recommender system makes recommendations depends on the type of data you have. If you just want to know which relationships happened in the past, you might be interested in collaborative filtering. If you have data describing users and the items they have interacted with (e.g. user’s age, restaurant food category, average movie review), you can model the likelihood of a new interaction taking these attributes into account. Filter by adding content and context.
Matrix factorization (MF) techniques underlie many popular algorithms, including word embeddings and topic modeling, and have become a dominant approach in collaborative filtering recommendation. MF can be used to compute ratings or similarity of user interactions for recommendation. In the simple user matrix below, Ted and Carol like movies B and C. Bob likes B-movies. To recommend a movie to Bob, the matrix factorization computes that users who liked B also liked C, so C might be Bob’s recommendation.
Matrix factorization using the Alternative Least Squares (ALS) algorithm gives the u-by-i user ranking matrix as the product of two dense matrices, the factors of the user and item matrices of size u × f and f × i (where u is number of users, i is the number of items, and f is the number of latent features). The factor matrix represents latent or hidden features that the algorithm is trying to discover. One matrix tries to describe the hidden or hidden features of each user, and one matrix tries to describe the hidden features of each movie. For each user and each item, the ALS algorithm iteratively learns (f) the numerical “factors” that represent the user or item. In each iteration, the algorithm alternately tunes one factor matrix and optimizes the other, and this process continues until convergence.
A matrix factorization library that optimizes alternative least squares (ALS) methods to solve very large-scale MF. CuMF uses a combination of techniques to maximize performance on single and multiple GPUs. These methods include intelligent access to sparse data that exploits GPU memory hierarchies, combined use of data parallelism and model parallelism to minimize the communication load between GPUs, and topology-aware parallelism reduction schemes.
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The proposed deep learning (DL) model builds on existing techniques such as factorization to model interactions between variables and embeddings that handle categorical variables. Collocations are learned numeric vectors representing object features such that similar objects (users or subjects) have similar distances in the vector space. For example, the deep learning approach of collaborative filtering learns user and object inputs (latent feature vectors) based on their interaction with a neural network.
DL methods are also applied to rapidly growing large-scale network architectures and optimization algorithms to train large amounts of data, extract features and build expression models using the power of deep learning.
Current DL-based recommender system models: DLRM, Wide and Deep (W&D), Neural Collaborative Filter (NCF), Variational AutoEncoder (VAE) and BERT (for NLP) form part of a portfolio of GPU-accelerated DL models covering a large range. Many network architectures and applications in many different domains beyond recommender systems, including image, text, and speech analysis. These models are designed and optimized for training with TensorFlow and PyTorch.
A Neural Collaborative Filtering (NCF) model is a neural network that provides collaborative filtering based on user-item interactions. This model considers matrix factorization from a nonlinear point of view. NCF TensorFlow takes as input a sequence of pairs (user ID, item ID) and feeds them to a matrix factorization stage (where entries are multiplied) and a multilayer perceptron (MLP) network, respectively.
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The results of the matrix factorization and MLP network are then combined and fed into a single dense layer that predicts whether the input user interacted with the input item.
Automatic neural networks use images in hidden layers to reconstruct the input layer in the output layer. Autoencoders for collaborative filtering study the non-linear representation of the user matrix and reconstruct it by identifying missing values.
GPU-Accelerated Variational Autoencoders for Collaborative Filtering (VAE-CF) is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering. VAE-CF is a neural network that provides collaborative filtering based on user-item interactions. The training data for the model consists of user-id pairs for each interaction between a user and an item.
The model consists of two parts: an encoder and a decoder. The encoder is a feed-forward, fully-connected neural network that transforms an input vector containing user-specific interactions into an n-dimensional variance distribution. This is a variable distribution