Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

doi:10.1016/J.ENG.2016.02.013

Engineering

2016, Vol. 2

Issue (2) : 212-224 https://doi.org/10.1016/J.ENG.2016.02.013

Research

非独立同分布推荐系统：推荐范式转换的综述和框架

Cao Longbing(

)

Advanced Analytics Institute, University of Technology Sydney, Sydney, NSW 2007, Australia

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摘要

虽然推荐系统在我们的生活、学习、工作和娱乐中扮演着越来越重要的角色，但是很多时候我们收到的推荐都是不相关的、重复的，或者包含不感兴趣的产品和服务。这些差的推荐系统产生的原因来源于一个本征假设：传统的理论和推荐系统认为用户和物品是独立同分布的(IID)。另一个明显的现象是，虽然投入了很多的精力模拟用户或者物品的特殊属性，但用户和物品的总体属性及它们之间的非独立同分布性(non-IID) 被忽略了。本文先讨论了推荐系统的非独立同分布性，紧接着介绍了非独立同分布性原理，目的是从耦合和异构性的角度来深入阐述传统的推荐系统的固有本质。这种非独立同分布推荐系统引起了传统推荐系统范式的转化—— 从独立同分布向非独立同分布进行转化，希望能够形成高效的、相关性高的、个人订制和可操作的推荐系统。这种系统创造了令人兴奋的能够解决包含冷启动、以稀疏数据为基础、跨域、基于群组信息和欺诈攻击等各种复杂情况的新的研究方向和解决方案。

Abstract：

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

Keywords Independent and identically distributed (IID) Non-IID Heterogeneity Coupling relationship Coupling learning Relational learning IIDness learning Non-IIDness learning Recommender system Recommendation Non-IID recommendation

通讯作者: Cao Longbing E-mail: longbing.cao@gmail.com

最新录用日期: 发布日期: 2016-07-04

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引用本文:

Longbing Cao. Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting[J]. Engineering, 2016, 2(2): 212-224.

网址:

http://journal.hep.com.cn/eng/EN/10.1016/J.ENG.2016.02.013 OR http://journal.hep.com.cn/eng/EN/Y2016/V2/I2/212

Fig.1 A systematic view of recommendation.

Fig.2 Non-IIDness in recommendation.

Fig.3 The multilayer model of recommendation research.

Fig.4 The four generations of recommendation research.

Fig.5 Framework for non-IID recommendation.

Tab.1 CMF versus CF on Movielens and Bookcrossing.

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