Yunwen Chen
Biographic Data
| ID | 6311801 |
|---|---|
| NAME | Yunwen Chen |
| GIVEN NAMES | Yunwen |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN Y |
| AFFILIATIONS | China Datang Corporation (China) |
| ORCID | 0000-0002-7493-5672 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 7 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
MLSA4Rec
Mphine-Kt
Contributions:This article proposes a new knowledge tracing (KT) model called heterogeneous information network embedding with metapath KT (MPHINE-KT) to model the knowledge state of learners and predict the accuracy of learners’ future academic performance. To mine the transfer relationships between different knowledge points in sequences, the learning experience of learners is designed. The effects of individuality and commonality between learn…
MetaGA
The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a sa…
Modeling Group Opinion Evolution on Online Social Networks
The research on group behavior is effective for establishing a good network environment since people in social networks tend to form groups spontaneously. Most studies on group behavior on online social networks assume that all individuals are reduced to one cluster, ignoring the existence of potential clusters and their importance in group opinion dynamics. This article introduces a novel group-gravitational field (GGF) model to investigate the …
Social Group Recommendation With TrAdaBoost
In recent years, group recommendation has become a research hotspot and focus in online social network community. Currently, several deep-learning-based approaches are leveraged to learn preferences of groups for items and predict the next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to the sparse group-item interactions. In order to address this problem, in this article, we introduce …
Deep Representation Learning for Location-Based Recommendation
Location-based recommendation has recently received a lot of attention in the communities of information service and mobile application. Its task is to provide personalized recommendations of points of interest (POIs) to users at a certain time and location. However, existing location-based recommendation models have at least two main drawbacks: first they cannot adequately capture semantic features of POIs and users, which may lead to unsatisfac…
Dismantling the fence for social justice? Evidence based on the inequity of urban green space accessibility in the central urban area of Beijing
Gated communities have become a common feature in recent decades and have been shown to lead to social inequality to the detriment of the poorest social classes. Because access to urban green space is crucial for both physical health and spiritual pleasure, it is often regarded as an indicator of social justice; however, there are many references to the current inequity in urban green space accessibility. Our study aimed first to measure the pote…
Exploring value capture mechanisms for heritage protection under public leasehold systems
Exploring value capture mechanisms for heritage protection under public leasehold systems
Social Group Recommendation With TrAdaBoost
In recent years, group recommendation has become a research hotspot and focus in online social network community. Currently, several deep-learning-based approaches are leveraged to learn preferences of groups for items and predict the next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to the sparse group-item interactions. In order to address this problem, in this article, we introduce …
Deep Representation Learning for Location-Based Recommendation
Location-based recommendation has recently received a lot of attention in the communities of information service and mobile application. Its task is to provide personalized recommendations of points of interest (POIs) to users at a certain time and location. However, existing location-based recommendation models have at least two main drawbacks: first they cannot adequately capture semantic features of POIs and users, which may lead to unsatisfac…
Dismantling the fence for social justice? Evidence based on the inequity of urban green space accessibility in the central urban area of Beijing
Gated communities have become a common feature in recent decades and have been shown to lead to social inequality to the detriment of the poorest social classes. Because access to urban green space is crucial for both physical health and spiritual pleasure, it is often regarded as an indicator of social justice; however, there are many references to the current inequity in urban green space accessibility. Our study aimed first to measure the pote…
Modeling Group Opinion Evolution on Online Social Networks
The research on group behavior is effective for establishing a good network environment since people in social networks tend to form groups spontaneously. Most studies on group behavior on online social networks assume that all individuals are reduced to one cluster, ignoring the existence of potential clusters and their importance in group opinion dynamics. This article introduces a novel group-gravitational field (GGF) model to investigate the …
MetaGA
The recommendation of long-tail items has been a persistent issue in recommender system research. The primary reason for this problem is that the model cannot learn better item features due to the lack of interactive record data of tail items, which leads to a decline in the model's recommendation performance. Existing methods transfer the features of the head items to the tail items, thereby ignoring their differences and failing to produce a sa…
MLSA4Rec
Mphine-Kt
Contributions:This article proposes a new knowledge tracing (KT) model called heterogeneous information network embedding with metapath KT (MPHINE-KT) to model the knowledge state of learners and predict the accuracy of learners’ future academic performance. To mine the transfer relationships between different knowledge points in sequences, the learning experience of learners is designed. The effects of individuality and commonality between learn…
Computer Science (5 works) · Artificial Intelligence (4 works) · Recommender Systems and Techniques (4 works) · Advanced Graph Neural Networks (3 works) · Data science (2 works) · Environmental planning (2 works) · Geography (2 works) · Machine learning (2 works) · Political science (2 works) · Psychology (2 works)