CosRec：2D卷积神经网络在序列推荐中的应用

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"CosRec:2D卷积神经网络在序列推荐中的应用" 现代推荐系统在理解和预测用户行为方面起着至关重要的作用，尤其在捕捉用户的序列模式方面。序列模式可以帮助揭示用户兴趣随时间的变化，从而提供更精准的个性化推荐。传统上，许多推荐系统依赖马尔科夫链和循环模型（如LSTM、GRU）来捕获这些模式。尽管这些模型在各种任务中表现出色，但它们在处理用户购买历史中嵌套的复杂关系时仍存在挑战。在此背景下，"CosRec: 2D卷积神经网络用于序列推荐"的研究提出了一个创新的方法。论文的作者包括An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan和Julian McAuley，他们都来自加州大学圣地亚哥分校。CosRec的核心思想是直接建模物品对之间的关系，以更有效地表示序列特征并捕获复杂的项目关联。 CosRec模型采用2D卷积神经网络架构，将用户的历史序列转化为三维张量。2D卷积滤波器用于学习局部特征，这使得模型能识别和学习序列中的局部模式，比如用户可能的购买习惯或兴趣变化。通过前向传播的方式，模型能够聚合高阶交互，进一步提升推荐的准确性。实验结果在两个公开数据集上验证了CosRec的有效性，与传统的推荐方法相比，它表现出了显著的优越性。这表明，2D卷积神经网络在处理序列推荐问题时，不仅能够处理复杂的用户行为模式，还能有效地利用空间上的邻近信息，为推荐系统带来性能的提升。此外，这种2D卷积的运用也带来了计算效率的优势。相比于循环神经网络，2D卷积通常具有更低的计算复杂度，可以更快地训练模型，这对于大规模推荐系统来说是极其重要的。 CosRec展示了2D卷积神经网络在序列推荐中的潜力，为推荐系统研究领域提供了一个新的视角和工具。通过直接建模物品对的交互，CosRec能够更准确地理解用户行为，并生成高质量的推荐序列。这种方法有望被应用于实际的在线服务，提升用户体验，同时增加商业价值。

CosRec: 2D Convolutional Neural Networks for Sequential

Recommendation

An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, Julian McAuley

University of California, San Diego

{ayan,scheng,wckang,m5wan,jmcauley}@ucsd.edu

ABSTRACT

Sequential patterns play an important role in building modern rec-

ommender systems. To this end, several recommender systems have

been built on top of Markov Chains and Recurrent Models (among

others). Although these sequential models have proven successful

at a range of tasks, they still struggle to uncover complex relation-

ships nested in user purchase histories. In this paper, we argue

that modeling pairwise relationships directly leads to an ecient

representation of sequential features and captures complex item

correlations. Specically, we propose a 2D convolutional network

for sequential recommendation (

CosRec

). It encodes a sequence

of items into a three-way tensor; learns local features using 2D

convolutional lters; and aggregates high-order interactions in a

feedforward manner.

Quantitative results on two public datasets show that our method

outperforms both conventional methods and recent sequence-based

approaches, achieving state-of-the-art performance on various eval-

uation metrics.

ACM Reference Format:

An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, Julian McAuley.

2019. CosRec: 2D Convolutional Neural Networks for Sequential Recom-

mendation. In The 28th ACM International Conference on Information and

Knowledge Management (CIKM’19), November 3–7, 2019, Beijing, China. ACM,

New York, NY, USA, 4 pages. https://doi.org/10.1145/3357384.3358113

1 INTRODUCTION

The goal of sequential recommendation is to predict users’ future

behavior based on their historical action sequences. Dierent from

traditional personalized recommendation algorithms (e.g. Matrix

Factorization [

]) which seek to capture users’ global tastes, se-

quential models introduce additional behavioral dynamics by taking

the order of users’ historical actions into consideration.

A classic line of work to model such dynamics is based on Markov

Chains (MCs), which assumes that a user’s next interaction is de-

rived from the preceding few actions only [

]. Recently, many

neural network based approaches have achieved success on this

task, where users’ complete interaction sequences can be incor-

porated through Recurrent Neural Networks (RNNs) [

] or Con-

volutional Neural Networks (CNNs) [

]. Note that most existing

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.

CIKM ’19, November 3–7, 2019, Beijing, China

ACM ISBN 978-1-4503-6976-3/19/11.. .$15.00

https://doi.org/10.1145/3357384.3358113

camera SD card lens tripod

… …

RNNs/CNNs/etc.

locally concentrated pattern

(a)

camera bike lens tripod

… …

2D CNN

Pair-Wise Encoding

camera

lens

(b)

Figure 1: Illustrations of (a) locally concentrated dynamics

and how they are preserved in existing models; and (b) an

example where ‘skip’ behavior (bike) exists between two

closely related items (camera and lens), and how this pattern

is preserved by the proposed framework.

models operate on ordered item representations directly, and thus

are constrained by the one-directional chain-structure of action

sequences. This leads to one advantage that these algorithms are

capable of preserving locally concentrated dynamics, e.g. as shown

in Figure 1a: consecutive purchases of a camera, a memory card,

and a camera lens may lead to a strong indication of buying a tripod.

In this paper

, we surprisingly nd that relaxing the above struc-

ture constraint may yield more eective recommendations. Speci-

cally, we propose a 2D CNN-based framework—2D

nvolutional

networks for

equential

rec

ommendation (

CosRec

). In particular,

we enable interactions among nonadjacent items by introducing a

simple but eective pairwise encoding module. As shown in Fig-

ure 1b, the ‘skip’ behavior within item sequences (i.e., the purchase

of a bike is less relevant to the context of consuming photography

products) may break the locality of the chain-structure but can be

easily bypassed through this pairwise encoding. On account of this

module, we show that standard 2D convolutional kernels can be

applied to solve sequential recommendation problems, where small

lters (e.g. 3

3) can be successfully incorporated. This also allows

us to build an extendable 2D CNN framework, which can be easily

adapted to either shallow or deep structures for dierent tasks.

2 RELATED WORK

Sequential recommendation methods typically seek to capture

sequential patterns among previously consumed items, in order

to accurately predict the next item. To this end, various mod-

els have been adopted, including Markov Chains (MCs) [

], Re-

current Neural Networks (RNNs) [

], Temporal Convolutional

Networks (TCNs) [

], and Self Attention [

], among others.

Session: Short - Recommendation

CIKM ’19, November 3–7, 2019, Beijing, China

2173

提出一种2D卷积

网络用于顺序推

荐算法

松散上面的结

构可以使推荐

系统更有效

对于不相邻的

item

引入了一个

简单有效地

成对编码模块

不相邻的item

比如：自行车

和消费摄影产品

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CosRec：2D卷积神经网络在序列推荐中的应用

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