Neda Mohammadi

Bio: Neda Mohammadi is an academic researcher from Ferdowsi University of Mashhad. The author has contributed to research in topics: Cloud computing & Order (exchange). The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

A two-stage location-sensitive and user preference-aware recommendation system

Abstract: Nowadays, cloud customers use cloud services increasingly to satisfy their demands. Usually, a significant number of customers are immature and inexpert and cannot express their needs accurately and numerically. They usually express their needs verbally and in the form of linguistic terms. On the other hand, the experienced customers express their needs numerically and accurately. In this situation, a recommendation system can be considered as one of the most useful ideas to support all type of customers. However, current recommendation systems (e.g., collaborative filtering based recommendations) meet customer requests that are accurately and numerically expressed. To support all types of customers, the construction of a strong recommendation system to analysis the demands expressed by customers (experienced and inexperienced) and to recommend suitable services is vital. As another important matter, cloud customers and services have been geographically distributed. Identifying the location of customers and services has a significant effect on the quality of services offered to customers. Therefore, the recommendation system should consider the location of customers and services in order to provide better services. In this paper, we introduce an efficient method to construct a powerful recommendation system which can provide suitable services considering the preferences of the customer and their location. The proposed recommendation system comprises two algorithms. The first algorithm is a fuzzy clustering algorithm, named FCA, that can well classify the location of customers and services. The second algorithm is an iterative adaptive neural-fuzzy algorithm, named IANFRA, which receives the preferences of the customer along with their location and identifies suitable services based on the locations clustered by FCA and the demands of customers (experienced and inexperienced). Finally, the feasibility of the proposed method has validated in terms of accuracy and scalability through conducting extensive experiments on a real distributed service quality dataset WS-DREAM. The evaluation results illustrate that both the service recommendation accuracy in the prediction of quality of services and the scalability, when the volume of the dataset is huge, have been improved.

Cloud Broker: A Systematic Mapping Study.

Abstract: The current systematic review includes a comprehensive 3-tier strategy (manual search, backward snowballing, and database search). The accuracy of the search methodology has been analyzed in terms of extracting related studies and collecting comprehensive and complete information in a supplementary file. In the search methodology, qualitative criteria have been defined to select studies with the highest quality and the most relevant among all search spaces. Also, some queries have been created using important keywords in the field under study in order to find studies related to the field of the cloud broker. Out of 1928 extracted search spaces, 171 search spaces have been selected based on defined quality criteria. Then, 1298 studies have been extracted from the selected search spaces and have been selected 496 high-quality papers published in prestigious journals, conferences, and workshops that the advent of them have been from 2009 until the end of 2019. In Systematic Mapping Study (SMS), 8 research questions have been designed to achieve goals such as identifying the most important and hottest topics in the field of cloud broker, identifying existing trends and issues, identifying active researchers and countries in the cloud broker field, a variety of commonly used techniques in building cloud brokers, variety of evaluation methods, the amount of research conducted in this field by year and place of publication and the identification of the most important active search spaces.

Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process

Abstract: Hybrid data mining processes are employed in recent days on several applications to achieve a better prediction and classification rate along with customer satisfaction. Hybrid data mining processes are the combination of different form of data considered for a neural network decision. In some cases, the different form of data represents image along with numerical data. In the proposed work, a food recommendation system is developed with respect to the flavour taste of the customer and considering the review comments of previous customers. The suggestions given by the users are taken into account as a feedback layer in the neural network for fine tuning the accuracy of the prediction process. The architectural design of the proposed model is employed with an ADNet (Adaptively Dense Convolutional Neural Network) algorithm to enable the usage of low range features in an efficient way. To verify the performance of the developed model, a pizza flavour recommender dataset is employed in the work for analysis. The experimental work analysis indicates that the ADNet algorithm works in a better way on a hybrid data analysis than the traditional DenseNet and ResNet algorithms.

A two-stage location-sensitive and user preference-aware recommendation system

Abstract: Nowadays, cloud customers use cloud services increasingly to satisfy their demands. Usually, a significant number of customers are immature and inexpert and cannot express their needs accurately and numerically. They usually express their needs verbally and in the form of linguistic terms. On the other hand, the experienced customers express their needs numerically and accurately. In this situation, a recommendation system can be considered as one of the most useful ideas to support all type of customers. However, current recommendation systems (e.g., collaborative filtering based recommendations) meet customer requests that are accurately and numerically expressed. To support all types of customers, the construction of a strong recommendation system to analysis the demands expressed by customers (experienced and inexperienced) and to recommend suitable services is vital. As another important matter, cloud customers and services have been geographically distributed. Identifying the location of customers and services has a significant effect on the quality of services offered to customers. Therefore, the recommendation system should consider the location of customers and services in order to provide better services. In this paper, we introduce an efficient method to construct a powerful recommendation system which can provide suitable services considering the preferences of the customer and their location. The proposed recommendation system comprises two algorithms. The first algorithm is a fuzzy clustering algorithm, named FCA, that can well classify the location of customers and services. The second algorithm is an iterative adaptive neural-fuzzy algorithm, named IANFRA, which receives the preferences of the customer along with their location and identifies suitable services based on the locations clustered by FCA and the demands of customers (experienced and inexperienced). Finally, the feasibility of the proposed method has validated in terms of accuracy and scalability through conducting extensive experiments on a real distributed service quality dataset WS-DREAM. The evaluation results illustrate that both the service recommendation accuracy in the prediction of quality of services and the scalability, when the volume of the dataset is huge, have been improved.

News Recommendation with Multi-views Emotion Analysis

Abstract: News recommendation aiming to find attractive news for users has been received many attentions in recent years. Existing news recommendation methods mainly focus on modeling user preference based on the interaction behaviors between users and news without the consideration of emotion information in the interaction. However, emotion information also plays an important role in improving the accuracy of news recommendation. In this paper, we propose an emotion analysis method for news recommendation with using multi-views to explore the impact of emotion information during the process of user's decision making. The emotion features extracted by the method are combined with the content features of the news to provide a comprehensive feature representation of the candidate news to improve the performance of recommendation. Experiments on real-world datasets show the effectiveness of the proposed method in improving accuracy of news recommendation.

Collaborative learning‐assisted recommendation of social trust over Web of Things

Abstract: Recent years have seen a surge in interest in the investigation of various recommender systems that are based on social networks. Because users' preferences are likely to be similar to or influenced by those of their connected friends, the integration of the social relationships that already exist between users has the potential to improve the accuracy of the recommendation results. To increase the quality of suggestions, a collaborative filtering recommendation algorithm that incorporates the latent social trust model is developed. Global trust value and expert model in a social matrix, and improved Pearson coefficient model make up the new social trust model. Social matrix, trust propagation model, and improved Pearson coefficient are the primary elements that contribute to the overall value of global trust. It is essential to have a sparse rating matrix and even a sparser social matrix to uncover potential social trust linkages that can improve the quality of recommendations. Pearson coefficient takes into account the ratings that users have given different items, but it does not take into account the items that users have in common with one another. The experimental findings suggest that including the new model into the recommendation algorithm improves the recommendation effect.