An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

ACM Trans. Knowl. Discov. Data

Zhu

Zhang

et al. 2022

The task of next Point-of-Interest (POI) recommendation aims to recommend a list of POIs for a user to visit at the next timestamp based on his/her previous interactions, which is valuable for both location-based service providers and users. Recent state-of-the-art studies mainly employ recurrent neural network (RNN) based methods to model user check-in behaviors according to user’s historical check-in sequences. However, most of the existing RNN-based methods merely capture geographical influences depending on physical distance or successive relation among POIs. They are insufficient to capture the high-order complex geographical influences among POI networks, which are essential for estimating user preferences. To address this limitation, we propose a novel Graph-based Spatial Dependency modeling (GSD) module, which focuses on explicitly modeling complex geographical influences by leveraging graph embedding. GSD captures two types of geographical influences, i.e., distance-based and transition-based influences from designed POI semantic graphs. Additionally, we propose a novel Graph-enhanced Spatial-Temporal network (GSTN) which incorporates user spatial and temporal dependencies for next POI recommendation. Specifically, GSTN consists of a Long Short-Term Memory (LSTM) network for user-specific temporal dependencies modeling and GSD for user spatial dependencies learning. Finally, we evaluate the proposed model using three real-world datasets. Extensive experiments demonstrate the effectiveness of GSD in capturing various geographical influences and the improvement of GSTN over state-of-the-art methods.

Section: Baselinesmentioning

confidence: 99%

Graph-Enhanced Spatial-Temporal Network for Next POI Recommendation

ACM Trans. Knowl. Discov. Data

Zhu

Zhang

et al. 2022

“…RNN is a kind of neural network with short-term memory capabilities to describe the relationship between the current output of a sequence and previous information [49]. In RNN, a neuron can receive not only information from other neurons but also its own information.…”

Section: B Model-based Cf Algorithmsmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

IEEE/CAA J. Autom. Sinica

Zhang

et al. 2021

With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

“…(3) CASER [40] is a recent approach that sequentially models the implicit user historical interactions with convolutional neural networks. It is a state-of-the-art recommendation model [13] that encodes the age property of reviews. (4) DeepCoNN [53] is a review-based recommendation model that jointly models users and items through a convolutional neural network.…”

Section: Baseline Approachesmentioning

confidence: 99%

Leveraging Review Properties for Effective Recommendation

Proceedings of the Web Conference 2021

Ounis

Macdonald

2021

Many state-of-the-art recommendation systems leverage explicit item reviews posted by users by considering their usefulness in representing the users' preferences and describing the items' attributes. These posted reviews may have various associated properties, such as their length, their age since they were posted, or their rating of the item. However, it remains unclear how these different review properties contribute to the usefulness of their corresponding reviews in addressing the recommendation task. In particular, users show distinct preferences when considering different aspects of the reviews (i.e. properties) for making decisions about the items. Hence, it is important to model the relationship between the reviews' properties and the usefulness of the reviews while learning the users' preferences and the items' attributes. In this paper, we propose to model the reviews with their associated available properties. We introduce a novel review properties-based recommendation model (RPRM) that learns which review properties are more important than others in capturing the usefulness of reviews, thereby enhancing the recommendation results. Furthermore, inspired by the users' information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users' preferences. We examine the effectiveness of RPRM using the well-known Yelp and Amazon datasets. Our results show that RPRM significantly outperforms a classical and five existing state-of-the-art baselines. Moreover, we experimentally show the advantages of using our proposed loss functions and negative sampling strategy, which further enhance the recommendation performances of RPRM.