Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-21
Author(s):  
Yi Ouyang ◽  
Bin Guo ◽  
Xing Tang ◽  
Xiuqiang He ◽  
Jian Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Apps ◽  
Rapid Development ◽  
Representation Learning ◽  
Adaptive Method ◽  
Mobile App ◽  
Cross Domain ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Novel Model

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

scholarly journals UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

2021 ◽  
Author(s):  
Jing Huang ◽  
Jie Yang
Keyword(s):  
Neural Networks ◽  
Message Passing ◽  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Challenging Problem ◽  
Unified Framework ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Research Domains

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4% to 88.8% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at https://github.com/OneForward/UniGNN.

scholarly journals The Role of Mobile Application Acceptance in Shaping E-Customer Service

2021 ◽  
Vol 13 (3) ◽  
pp. 77
Author(s):  
Laith T. Khrais ◽  
Abdullah M. Alghamdi
Keyword(s):  
Customer Service ◽  
Mobile Application ◽  
Mobile Apps ◽  
Rapid Development ◽  
Mobile App ◽  
Ease Of Use ◽  
Adoption Of Technology ◽  
Customer Needs ◽  
Online Retailers

Most retailers are integrating their practices with modern technologies to enhance the effectiveness of their operations. The adoption of technology aims to enable businesses to accurately meet customer needs and expectations. This study focused on examining the role of mobile application (app) acceptance in shaping customer electronic experience. A mixed method was adopted, in which qualitative data were collected using interviews, and quantitative data were gathered using the questionnaires. The results indicate that mobile app acceptance contributes to a positive customer experience while purchasing products and services from online retailers. Mobile apps are associated with benefits, such as convenience, ease of use, and the ability to access various products and services. With the rapid development in technology, e-commerce retailers should leverage such innovations to meet customer needs.

scholarly journals A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 21
Author(s):  
Jianfei Li ◽  
Yongbin Wang ◽  
Zhulin Tao
Keyword(s):  
Neural Network ◽  
Latent Factors ◽  
Latent Factor ◽  
Information Granularity ◽  
Node Attribute ◽  
Item Attribute ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Granularity Distribution

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

scholarly journals Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

2022 ◽  
Vol 16 (2) ◽  
pp. 1-18
Author(s):  
Hanlu Wu ◽  
Tengfei Ma ◽  
Lingfei Wu ◽  
Fangli Xu ◽  
Shouling Ji
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Superior Performance ◽  
True Label ◽  
Label Aggregation ◽  
Correlation Information ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
High Level

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.

scholarly journals MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

2019 ◽  
Author(s):  
Nuo Xu ◽  
Pinghui Wang ◽  
Long Chen ◽  
Jing Tao ◽  
Junzhou Zhao
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Dual Graph ◽  
Material Design ◽  
Drug Regimen ◽  
Short Term ◽  
New Material ◽  
Long Short Term Memory ◽  
Real World Datasets ◽  
Graph Neural Networks

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present {\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.

scholarly journals Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

2022 ◽  
Vol 40 (4) ◽  
pp. 1-46
Author(s):  
Hao Peng ◽  
Ruitong Zhang ◽  
Yingtong Dou ◽  
Renyu Yang ◽  
Jingyi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Message Passing ◽  
Representation Learning ◽  
Graph Representations ◽  
Graph Data ◽  
Neighborhood Information ◽  
Neighborhood Selection ◽  
Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data, typically through message passing among nodes by aggregating their neighborhood information via different operations. While promising, most existing GNNs oversimplify the complexity and diversity of the edges in the graph and thus are inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this article, we propose RioGNN , a novel Reinforced, recursive, and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining relation-dependent representations. We first construct a multi-relational graph, according to the practical task, to reflect the heterogeneity of nodes, edges, attributes, and labels. To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes. A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding. Particularly, to improve the efficiency of neighbor selecting, we propose a new recursive and scalable reinforcement learning framework with estimable depth and width for different scales of multi-relational graphs. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism. Comprehensive experiments on real-world graph data and practical tasks demonstrate the advancements of effectiveness, efficiency, and the model explainability, as opposed to other comparative GNN models.

scholarly journals Application of Mobile App in English Teaching in an Intelligent Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haihua Tu
Keyword(s):  
Mobile Learning ◽  
Mobile Apps ◽  
Rapid Development ◽  
Social Economy ◽  
Modern Society ◽  
Mobile App ◽  
Intelligent Environment ◽  
English Teaching ◽  
And Control ◽  
Learning English

With the rapid development of social economy and technology, the application of technology in the field of education has also been further developed. The past paper information carriers can no longer fully meet the development needs of the modern society. In this context, this thesis explores a learning model that combines mobile learning based on mobile apps with English teaching. The school’s hardware facilities have been continuously updated with the development of social information technology, and intelligent voice systems have been continuously introduced into classrooms. This article focuses on exploring the application of mobile app in English teaching in a smart environment, using the implementation of the mobile learning English model, and dividing two identical classes into an experimental class and control class through questionnaires and comparisons. The experimental design finally concluded that the use of mobile app to assist English teaching in all aspects of the relative improvement of the English performance of the class, the teaching effect is obvious, effectively improves students' interest in learning English and, at the same time, realizes students’ mobile learning, changes the scope of teaching activities from classroom changes to a wider range, and plays a role in consolidating and supplementing classroom English.

scholarly journals Memory Augmented Graph Neural Networks for Sequential Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 5045-5052 ◽  
Author(s):  
Chen Ma ◽  
Liheng Ma ◽  
Yingxue Zhang ◽  
Jianing Sun ◽  
Xue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Performance Metrics ◽  
Contextual Information ◽  
Short Term ◽  
User Interests ◽  
User Behaviors ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Occurrence Patterns

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.

scholarly journals The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

2019 ◽  
Vol 13 (12) ◽  
pp. 67
Author(s):  
Yulius Harjoseputro ◽  
Yonathan Dri Handarkho ◽  
Heronimus Tresy Renata Adie
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Rapid Development ◽  
Mobile App ◽  
Convolution Neural Network ◽  
Mobile Technologies ◽  
Mobile Client ◽  
Computation Cost ◽  
Program Interface ◽  
Server Architecture

<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

scholarly journals Attention-Enhanced Graph Neural Networks for Session-Based Recommendation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1607 ◽  
Author(s):  
Baocheng Wang ◽  
Wentao Cai
Keyword(s):  
Critical Role ◽  
User Needs ◽  
Short Term ◽  
User Interests ◽  
Recommendation Algorithms ◽  
Multiple Levels ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Novel Model

Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search and e-commerce). Existing recommendation algorithms usually model a session as a sequence or a session graph to model transitions between items. Despite their effectiveness, we would argue that the performance of these methods is still flawed: (1) Using only fixed session item embedding without considering the diversity of users’ interests and target items. (2) For user’s long-term interest, the difficulty of capturing the different priorities for different items accurately. To tackle these defects, we propose a novel model which leverages both the target attentive network and self-attention network to improve the graph-neural-network (GNN)-based recommender. In our model, we first model user’s interaction sequences as session graphs which serves as the input of the GNN, and each node vector involved in session graph can be obtained via the GNN. Next, target attentive network can activates different user interests corresponding to varied target items (i.e., the session embedding learned varies with different target items), which can reveal the relevance between users’ interests and target items. At last, after applying the self-attention mechanism, the different priorities for different items can be captured to improve the precision of the long-term session representation. By using a hybrid of long-term and short-term session representation, we can capture users’ comprehensive interests at multiple levels. Extensive experiments demonstrate the effectiveness of our algorithm on two real-world datasets for session-based recommendation.

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