Sequential Recommender Systems: Challenges, Progress and Prospects
∗
Shoujin Wang
1
, Liang Hu
2
, Yan Wang
1
, Longbing Cao
2
, Quan Z. Sheng
1
, Mehmet Orgun
1
1
Department of Computing, Macquarie University
2
Advanced Analytics Institute, University of Technology Sydney
{shoujin.wang, yan.wang}@mq.edu.au, rainmilk@gmail.com,
Abstract
The emerging topic of sequential recommender
systems (SRSs) has attracted increasing attention
in recent years. Different from the conventional
recommender systems (RSs) including
collaborative filtering and content-based filtering,
SRSs try to understand and model the sequential
user behaviors, the interactions between users
and items, and the evolution of users’ preferences
and item popularity over time. SRSs involve the
above aspects for more precise characterization of
user contexts, intent and goals, and item
consumption trend, leading to more accurate,
customized and dynamic recommendations. In
this paper, we provide a systematic review on SRSs.
We first present the characteristics of SRSs, and
then summarize and categorize the key challenges
in this research area, followed by the
corresponding research progress consisting of the
most recent and representative developments on
this topic. Finally, we discuss the important
research directions in this vibrant area.
1 Introduction
Sequential recommender systems (SRSs) suggest items
which may be of interest to a user by mainly modelling the
sequential dependencies over the user-item interactions
(e.g., view or purchase items on an online shopping platform)
in a sequence [27]. The traditional recommender systems
(RSs), including the content-based and collaborative filtering
RSs, model the user-item interactions in a static way and can
only capture the users’ general preferences. In contrast,
SRSs treat the user-item interactions as a dynamic sequence
and take the sequential dependencies into account to
capture the current and recent preference of a user for more
accurate recommendation [1]. In order to enhance the
understanding of SRSs, next we present the motivation and
formalization of SRSs.
A comprehensive survy on session-based recommender systems
can be found: https://arxiv.org/abs/1902.04864.
Figure 1: Two examples of SRSs: (1) After Jimmy has booked a flight,
a hotel and rented a car, what will be his next action? (2) After Tina
has bought an iPhone, an iWatch and a pair of AirPods, what would
she buy next?
Motivation: Why Sequential Recommender Systems?
The user-item interactions are essentially sequentially
dependent. In the real world, users’ shopping behaviours
usually happen successively in a sequence, rather than in an
isolated manner. Taking the shopping events of Jimmy
depicted in Figure 1 as an example, before Jimmy started
holiday, he booked a flight and a hotel and rented a car
successively, and his next action may be visiting a tourist
attraction via selfdriving. In such a case, the hotel may be
close to the destination airport of the flight and the location
for picking up the rented car may be not far away from the
hotel. In this scenario, each of Jimmy’s next actions depends
on the prior ones and thus all the four consumption actions
are sequentially dependent. Likewise, we can see the
sequential dependencies in Tina’s case. Such kind of
sequential dependencies commonly exist in transaction data
but cannot be well captured by the conventional content-
based RSs or collaborative filtering RSs [12], which
essentially motivates the development of SRSs.
Both the users’ preference and items’ popularity are
dynamic rather than static over time. In fact, a user’s
preference and taste may change over time. For instance,
many young people who used to be iPhone fans now have
switched to become fans of the phones manufactured by
Huawei or Samsung and the popularity of iPhone has been
dropping in recent years. Such dynamics are of great
significance for precisely profiling a user or an item for more
accurate recommendations and they can only be captured
by SRSs.
Tina
Jimmy
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