Enhanced Recommendation Systems: A Survey on the Impact of Auxiliary Information

Navansh Goel, Suganeshwari G
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Abstract

In the age of big data, recommendation systems have become a critical tool for helping users navigate the overwhelming amount of online information. Enhanced recommendation systems take this one step further, leveraging the latest algorithms and data-driven insights to deliver highly personalized and relevant recommendations. This research paper provides a comprehensive overview of the recent progress in enhanced recommendation systems, covering the current state-of-the-art techniques and discussing the opportunities and challenges practitioners face. The article explores a range of approaches, including deep learning techniques and hybrid models that integrate both user and item data, and presents the essential concepts, methods, and applications driving the advancement of recommendation systems. We recognize the pressing hurdles in the field as sparsity and diversity, thereby focusing on intent-based models that exploit the additional/auxiliary information by aggregating implicit feedback from user-item interactions. We have gone one step further by compiling the benchmarks in the field, enabling new researchers to explore and innovate at a much more thoughtful and faster pace .
增强型推荐系统:辅助信息的影响调查
在大数据时代,推荐系统已经成为帮助用户浏览海量在线信息的关键工具。增强型推荐系统更进一步,利用最新的算法和数据驱动的洞察力来提供高度个性化和相关的推荐。本研究报告全面概述了增强型推荐系统的最新进展,涵盖了当前最先进的技术,并讨论了从业者面临的机遇和挑战。本文探讨了一系列方法,包括深度学习技术和集成用户和项目数据的混合模型,并介绍了推动推荐系统进步的基本概念、方法和应用程序。我们认识到该领域的紧迫障碍是稀疏性和多样性,因此专注于基于意图的模型,该模型通过聚合来自用户-物品交互的隐式反馈来利用附加/辅助信息。我们已经通过汇编该领域的基准更进一步,使新的研究人员能够以更周到和更快的速度探索和创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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