Research on Customer Loyalty of Online Short-term Rental Service: A Meta-analysis

Online short-term rental service has developed rapidly recently. Various scholars focused on how to improve customer loyalty of online short-term rental service, but their conclusions are usually different. Therefore, we built a comprehensive analysis to derive a unified conclusion. A meta-analysis was conducted according to the effect sizes extracted from 35 empirical articles about customer loyalty of online short-term rental service. The effect of customer loyalty classification was further explored from the two sub-dimensions, behavioral loyalty and composite loyalty. The results of the main effect analysis show that only sustainability has no significant effect on attitude. The loyalty classification analysis proves the validity and particularity of the results from the perspective of sub-dimensions of loyalty. The conclusions of this study will bring significant enlightenment to the academic and industry

Predicting Stock Price Movement Direction with Enterprise Knowledge Graph

Predicting stock price movement direction is a challenging task for financial investment. Previous researches focused on investigating the impacts of external factors (e.g., big events, economic influence and sentiments) in combination with the historical price to predict short-term stock price movement, while few researches leveraged the power of various relationships among enterprises. To bridge this gap, this research proposes power vector model and influence propagation model to mine the rich information in constructed Enterprise Knowledge Graph (EKG) for price movement prediction. In addition, Deep Neural Network (DNN) is introduced to train the model. The proposed model shows good prediction performance on the dataset of China top 500 enterprises

Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation

As location-based services (LBS) have grown in popularity, the collection of human mobility data has become increasingly extensive to build machine learning (ML) models offering enhanced convenience to LBS users. However, the convenience comes with the risk of privacy leakage since this type of data might contain sensitive information related to user identities, such as home/work locations. Prior work focuses on protecting mobility data privacy during transmission or prior to release, lacking the privacy risk evaluation of mobility data-based ML models. To better understand and quantify the privacy leakage in mobility data-based ML models, we design a privacy attack suite containing data extraction and membership inference attacks tailored for point-of-interest (POI) recommendation models, one of the most widely used mobility data-based ML models. These attacks in our attack suite assume different adversary knowledge and aim to extract different types of sensitive information from mobility data, providing a holistic privacy risk assessment for POI recommendation models. Our experimental evaluation using two real-world mobility datasets demonstrates that current POI recommendation models are vulnerable to our attacks. We also present unique findings to understand what types of mobility data are more susceptible to privacy attacks. Finally, we evaluate defenses against these attacks and highlight future directions and challenges.Comment: 26 page

Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators

Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure.} FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11% on CIFAR-10 and CINIC-10 datasets

Properties of Crushed Red-Bed Soft Rock Mixtures Used in Subgrade

Slaking red-bed soft rocks are widely distributed in the south of Anhui Province, China, and several highways will go through this area. It is important to evaluate their physical and mechanical characteristics for the purpose of using this kind of soft rocks as materials for road construction. In this paper, the compacting tests, the resilient modulus tests, the California bearing ratio (CBR) tests, and permeability tests have been carried out on crushed red-bed soft rock mixtures. The test results showed that, for a given degree of compaction, the resilient modulus decreases linearly with the increase of moisture content. For a given moisture content, the resilient modulus and CBR values increase linearly with the increase of compaction degree, while the soaking swelling, water absorption capacity, and permeability coefficient decrease linearly. In other words, the strength and water stability are enhanced with the increase of the degree of compaction. The results demonstrate that the crushed red-bed soft rock mixtures can be directly used as materials for the highway construction by taking corresponding measures