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content-based recommender system

A content-based recommender system is an information filtering system that suggests items to a user by comparing the characteristics or features of items with a profile of that user past preferences. This approach operates on the principle that a user is likely to be interested in items that are similar in content, metadata, or attributes to items they have previously interacted with or rated positively. These systems analyze descriptive data, such as keywords, genres, text descriptions, or extracted feature representations, and match them against the documented interests of an individual user using similarity measures. Unlike collaborative filtering, which depends on behavioral patterns and ratings from a broader community of users, content-based recommender systems rely solely on individual user histories and item properties, allowing them to recommend newly added items without needing ratings from other users.

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Deep Learning Based Recommender System

Deep Learning Based Recommender System

Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay

OrganizationsNanyang Technological UniversityUniversity of New South Wales

Why you should read this

Provides a comprehensive taxonomy of deep learning recommender systems, synthesizing state-of-the-art architectures and outlining critical open directions for future research.

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 web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.

Added

2026-09-25