Graph-Based Stock Recommendation by Time-Aware Relational Attention Network

2021 ◽  
Vol 16 (1) ◽  
pp. 1-21
Author(s):  
Jianliang Gao ◽  
Xiaoting Ying ◽  
Cong Xu ◽  
Jianxin Wang ◽  
Shichao Zhang ◽  
...  
Keyword(s):  
Future Price ◽  
Attention Network ◽  
Stock Recommendation ◽  
Investment Returns ◽  
Real World Datasets ◽  
Stock Market Investors ◽  
Historical Sequences ◽  
Relation Graph ◽  
Near Future ◽  
Time Aware

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

scholarly journals DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

2020 ◽  
Vol 34 (01) ◽  
pp. 930-937
Author(s):  
Qingxiong Tan ◽  
Mang Ye ◽  
Baoyao Yang ◽  
Siqi Liu ◽  
Andy Jinhua Ma ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Medical Knowledge ◽  
Attention Mechanism ◽  
Physiological Variables ◽  
Real World Datasets ◽  
Clinical Records ◽  
Gated Recurrent Unit ◽  
Time Aware

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

scholarly journals Feature-level Deeper Self-Attention Network for Sequential Recommendation

2019 ◽  
Author(s):  
Tingting Zhang ◽  
Pengpeng Zhao ◽  
Yanchi Liu ◽  
Victor S. Sheng ◽  
Jiajie Xu ◽  
...  
Keyword(s):  
Experimental Results ◽  
Sequential Patterns ◽  
Internet Applications ◽  
Attention Network ◽  
Item Level ◽  
Heterogeneous Features ◽  
Novel Method ◽  
Fully Connected ◽  
Near Future

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. In this paper, we propose a novel method named Feature-level Deeper Self-Attention Network (FDSA) for sequential recommendation. Specifically, FDSA first integrates various heterogeneous features of items into feature sequences with different weights through a vanilla mechanism. After that, FDSA applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Then, we integrate the outputs of these two blocks to a fully-connected layer for next item recommendation. Finally, comprehensive experimental results demonstrate that considering the transition relationships between features can significantly improve the performance of sequential recommendation.

Trust Prediction for Online Social Networks with Integrated Time-Aware Similarity

2021 ◽  
Vol 15 (6) ◽  
pp. 1-30
Author(s):  
Xiaofeng Gao ◽  
Wenyi Xu ◽  
Mingding Liao ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Matrix Factorization ◽  
Online Social Networks ◽  
User Similarity ◽  
Trust Prediction ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency ◽  
Real World Datasets ◽  
Prediction Approach ◽  
Time Aware

Online social networks gain increasing popularity in recent years. In online social networks, trust prediction is significant for recommendations of high reputation users as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and -NN search. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this article, we propose a novel trust prediction approach named iSim : an integrated time-aware similarity-based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, time-aware matrix factorization, and propagated trust. This article is the first work in the literature employing time-aware matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of iSim , and provide its theoretical time bound. Moreover, we also provide the detailed overview and theoretical analysis of the existing works. Finally, the extensive experiments with real-world datasets show that iSim achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.

scholarly journals Multilevel Combinatorial Optimization across Quantum Architectures

2021 ◽  
Vol 2 (1) ◽  
pp. 1-29
Author(s):  
Hayato Ushijima-Mwesigwa ◽  
Ruslan Shaydulin ◽  
Christian F. A. Negre ◽  
Susan M. Mniszewski ◽  
Yuri Alexeev ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Combinatorial Optimization Problems ◽  
Large Scale Problems ◽  
Quantum Processor ◽  
Partitioning Problem ◽  
Real World Datasets ◽  
Near Future

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution. Reproducibility : Our code and data are available at Reference [1].

scholarly journals A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

2021 ◽  
Vol 10 (5) ◽  
pp. 336
Author(s):  
Jian Yu ◽  
Meng Zhou ◽  
Xin Wang ◽  
Guoliang Pu ◽  
Chengqi Cheng ◽  
...  
Keyword(s):  
Real World ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Context Aware ◽  
Trajectory Prediction ◽  
Attention Network ◽  
Series Of Experiments ◽  
Real World Datasets ◽  
The Moment ◽  
Autonomous Driving System

Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results.

scholarly journals INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process

2018 ◽  
Author(s):  
Ruocheng Guo ◽  
Jundong Li ◽  
Huan Liu
Keyword(s):  
Point Process ◽  
Predictive Power ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Sharing Economy ◽  
Event Data ◽  
Intractable Likelihood ◽  
Sequential Event ◽  
Real World Datasets ◽  
Near Future

Copious sequential event data has consistently increased in various high-impact domains such as social media and sharing economy. When events start to take place in a sequential fashion, an important question arises: "what type of event will happen at what time in the near future?" To answer the question, a class of mathematical models called the marked temporal point process is often exploited as it can model the timing and properties of events seamlessly in a joint framework. Recently, various recurrent neural network (RNN) models are proposed to enhance the predictive power of mark temporal point process. However, existing marked temporal point models are fundamentally based on the Maximum Likelihood Estimation (MLE) framework for the training, and inevitably suffer from the problem resulted from the intractable likelihood function. Surprisingly, little attention has been paid to address this issue. In this work, we propose INITIATOR - a novel training framework based on noise-contrastive estimation to resolve this problem. Theoretically, we show the exists a strong connection between the proposed INITIATOR and the exact MLE. Experimentally, the efficacy of INITIATOR is demonstrated over the state-of-the-art approaches on several real-world datasets from various areas.

scholarly journals Graph Contextualized Self-Attention Network for Session-based Recommendation

2019 ◽  
Author(s):  
Chengfeng Xu ◽  
Pengpeng Zhao ◽  
Yanchi Liu ◽  
Victor S. Sheng ◽  
Jiajie Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Attention Mechanism ◽  
The Self ◽  
Graph Structure ◽  
Convolutional Network ◽  
Attention Network ◽  
Attention Model ◽  
Sequence Modeling ◽  
Real World Datasets

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming).  Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences.  In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN).  Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.

scholarly journals An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions

2018 ◽  
Vol 8 (12) ◽  
pp. 2426 ◽  
Author(s):  
Ruo Huang ◽  
Shelby McIntyre ◽  
Meina Song ◽  
Haihong E ◽  
Zhonghong Ou
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
User Profile ◽  
Vital Role ◽  
Short Term ◽  
Novel Approach ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Near Future

Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.

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