深度记忆网络：融合推荐系统中的协同过滤与记忆模型

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2018年的论文《Collaborative Memory Network for Recommendation Systems》由Travis Ebesu、Bin Shen和Yi Fang共同撰写，他们分别来自圣克拉拉大学计算机工程系和谷歌公司。这篇研究着重于深度学习在推荐系统中的应用，尤其是在解决传统协同过滤（CF）模型的局限性方面。传统的协同过滤方法主要分为两种类型：基于用户的协同过滤和基于物品的协同过滤，但这些方法往往忽视了另一种重要的模型类别——邻域或记忆型方法。随着深度学习在众多领域取得显著进步，该领域的研究人员开始探索如何将深度学习架构与协同过滤模型结合，以提升推荐系统的性能。作者提出了一种名为Collaborative Memory Networks (CMN) 的新型深度学习架构。CMN旨在融合全球隐含因子模型的全局结构优势和局部邻域模型的优点，通过非线性方式整合这两类模型。CMN的核心思想借鉴了Memory Networks的成功之处，其中包含一个记忆组件和神经注意力机制，作为邻域组成部分，使得模型能够更有效地捕捉用户和项目之间的复杂关系。具体来说，CMN的工作原理可能包括以下几个关键步骤： 1. **记忆组件**：通过存储用户的偏好历史数据和项目的特征，构建一个动态的记忆库，这有助于捕捉用户的长期兴趣和项目的独特属性。 2. **神经注意力机制**：利用注意力机制，模型可以根据当前的查询（例如用户的行为）自动聚焦于记忆库中最具相关性的部分，避免了传统方法中的信息过载问题。 3. **非线性融合**：通过深度神经网络处理和整合记忆组件中的信息，使得模型能够进行更复杂的模式识别和预测，从而提供更精确的个性化推荐。 4. **训练与优化**：采用反向传播算法来训练模型参数，以最小化推荐误差，确保推荐的准确性和用户满意度。 5. **实验评估**：论文中可能包含了实验证据，展示了CMN在各种评价指标（如召回率、精度和多样性）上的表现，以及与传统模型如矩阵分解和深度神经网络的对比分析。这篇论文为推荐系统的研究带来了新颖的视角，通过深度学习和记忆机制的巧妙结合，它不仅扩展了深度学习在推荐领域的应用范围，还可能提高了推荐的精准度和用户体验，对未来的个性化推荐有着深远的影响。

Collaborative Memor y Network for Recommendation Systems

Travis Ebesu

Santa Clara University

Department of Computer Engineering

Santa Clara, CA, USA

tebesu@scu.edu

Bin Shen

Google

New York, NY, USA

bshen@google.com

Yi Fang

Santa Clara University

Department of Computer Engineering

Santa Clara, CA, USA

yfang@scu.edu

ABSTRACT

Recommendation systems play a vital role to keep users engaged

with personalized content in modern online platforms. Deep learn-

ing has revolutionized many research elds and there is a recent

surge of interest in applying it to collaborative ltering (CF). How-

ever, existing methods compose deep learning architectures with

the latent factor model ignoring a major class of CF models, neigh-

borhood or memory-based approaches. We propose Collaborative

Memory Networks (CMN), a deep architecture to unify the two

classes of CF models capitalizing on the strengths of the global struc-

ture of latent factor model and local neighborhood-based structure

in a nonlinear fashion. Motivated by the success of Memory Net-

works, we fuse a memory component and neural attention mecha-

nism as the neighborhood component. The associative addressing

scheme with the user and item memories in the memory module

encodes complex user-item relations coupled with the neural atten-

tion mechanism to learn a user-item specic neighborhood. Finally,

the output module jointly exploits the neighborhood with the user

and item memories to produce the ranking score. Stacking multiple

memory modules together yield deeper architectures capturing

increasingly complex user-item relations. Furthermore, we show

strong connections between CMN components, memory networks

and the three classes of CF models. Comprehensive experimen-

tal results demonstrate the eectiveness of CMN on three public

datasets outperforming competitive baselines. Qualitative visual-

ization of the attention weights provide insight into the model’s

recommendation process and suggest the presence of higher order

interactions.

CCS CONCEPTS

• Information systems → Recommender systems

;

• Comput-

ing methodologies → Neural networks;

KEYWORDS

deep learning; memory networks; collaborative ltering

ACM Reference Format:

Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative Memory Network

for Recommendation Systems. In SIGIR ’18: The 41st International ACM

Permission to make digital or hard copies of all or part of this work for personal or

classroom use is granted without fee provided that copies are not made or distributed

for prot or commercial advantage and that copies bear this notice and the full citation

on the rst page. Copyrights for components of this work owned by others than ACM

must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,

to post on servers or to redistribute to lists, requires prior specic permission and/or a

fee. Request permissions from permissions@acm.org.

SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA

ACM ISBN 978-1-4503-5657-2/18/07.. . $15.00

https://doi.org/10.1145/3209978.3209991

SIGIR Conference on Research & Development in Information Retrieval, July

8–12, 2018, Ann Arbor, MI, USA. ACM, New York, NY, USA, 10 pages. https:

//doi.org/10.1145/3209978.3209991

1 INTRODUCTION

Recommendation systems are vital to keeping users engaged and

satised with personalized recommendations in the age of infor-

mation explosion. Users expect personalized content in modern

E-commerce, entertainment and social media platforms but the ef-

fectiveness of recommendations are restricted by existing user-item

interactions and model capacity. The ability to leverage higher or-

der reasoning may help alleviate the problem of sparsity. A popular

and successful technique, collaborative ltering (CF), establishes

the relevance between users and items from past interactions (e.g.,

clicks, ratings, purchases) by assuming similar users will consume

similar items.

CF can generally be grouped in three categories: memory or

neighborhood-based approaches, latent factor models and hybrid

models [

]. Memory or neighborhood-based methods form

recommendations by identifying groups or neighborhoods of sim-

ilar users or items based on the previous interaction history. The

simplicity of these models such as item

nearest neighbor (KNN)

have shown success in production systems at Amazon [

]. Latent

factor models such as matrix factorization project each user and

item into a common low dimensional space capturing latent rela-

tions. Neighborhood methods capture local structure but typically

ignore the mass majority of ratings available due to selecting at

most

observations from the intersection of feedback between two

users or items [

]. On the other hand, latent factor models capture

the overall global structure of the user and item relationships but

often ignore the presence of a few strong associations. The follow-

ing weaknesses between the local neighborhood-based and global

latent factor models lead to the development of hybrid models such

as SVD++ [

] and generalizations such as Factorization Machines

[

] which integrate both neighborhood-based approaches and

latent factor models to enrich predictive capabilities.

Recently, deep learning has made massive strides in many re-

search areas obtaining state of the art performance in computer

vision [

], question answering [

], learning programs

[

], machine translation [

] and many other domains. The suc-

cessful integration of deep learning methods in recommendation

systems have demonstrated the noticeable advantages of com-

plex nonlinear transformations over traditional linear models [

However, existing composite architectures incorporate the latent

factor model ignoring the integration of neighborhood-based ap-

proaches in a nonlinear fashion. Hence, we propose to represent the

neighborhood-based component with a Memory Network [

]

to capture higher order complex relations between users and items.

arXiv:1804.10862v2 [cs.IR] 21 Jun 2018

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