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A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation | IEEE Journals & Magazine | IEEE Xplore

A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation


Abstract:

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models...Show More

Abstract:

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers and practitioners working on recommender systems. Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative work for each type, we finally discuss some promising directions in this field.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 35, Issue: 5, 01 May 2023)
Page(s): 4425 - 4445
Date of Publication: 25 January 2022

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1 Introduction

Information overload is an increasing problem in people's every life due to the proliferation of the Internet. Recommender system serves as an effective solution to alleviate the information overload issue, to facilitate users seeking desired information, and to increase the traffic and revenue of service providers. It has been used in a wide range of applications, such as e-commerce, social media sites, news portals, app stores, digital libraries, and so on. It is one of the most ubiquitous user-centered artificial intelligence applications in modern information systems.

References

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