Recommender system ≈ Recommender system
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Neural Collaborative Filtering Open
10.1145/3038912.3052569
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Wide & Deep Learning for Recommender Systems Open
Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature tr…
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Deep Neural Networks for YouTube Recommendations Open
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep lea…
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Open
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or requir…
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Graph Neural Networks for Social Recommendation Open
In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to a…
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DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network Open
The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of f…
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Variational Autoencoders for Collaborative Filtering Open
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate coll…
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DKN Open
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing …
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Joint Deep Modeling of Users and Items Using Reviews for Recommendation Open
A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of rec…
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Deep Matrix Factorization Models for Recommender Systems Open
Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for …
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VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback Open
Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user f…
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A Survey on Knowledge Graph-Based Recommender Systems Open
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences. Although numerous efforts have been made toward more personalized r…
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Recurrent Recommender Networks Open
Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-…
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Explainable Reasoning over Knowledge Graphs for Recommendation Open
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which pro…
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Deep Learning based Recommender System: A Survey and New Perspectives Open
With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many w…
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What do we need to build explainable AI systems for the medical domain? Open
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep lear…
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DRN Open
In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Altho…
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Neural Attentional Rating Regression with Review-level Explanations Open
Reviews information is dominant for users to make online purchasing decisions in e-commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews hurt model's performance, and are also less meaningful for user's…
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Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences Open
Incorporating knowledge graph (KG) into recommender system is promising in\nimproving the recommendation accuracy and explainability. However, existing\nmethods largely assume that a KG is complete and simply transfer the\n"knowledge" in K…
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Graph Contextualized Self-Attention Network for Session-based Recommendation Open
Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieve…
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A Simple Convolutional Generative Network for Next Item Recommendation Open
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-d…
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Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach Open
Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborat…
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NAIS: Neural Attentive Item Similarity Model for Recommendation Open
Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as …
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Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks Open
Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in info…
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Time Interval Aware Self-Attention for Sequential Recommendation Open
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently. Traditionally, Markov Chains(MCs), and more recently Recurrent Neu…
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A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions Open
Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive revi…
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A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields Open
This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation m…
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Collaborative Metric Learning Open
Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (C…
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Open
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or requir…
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Contrastive Learning for Sequential Recommendation Open
Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions. Despite their success, we argue that these approaches u…