Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep lear...Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.展开更多
A computer network can be defined as many computing devices connected via a communication medium like the internet.Computer network development has proposed how humans and devices communicate today.These networks have...A computer network can be defined as many computing devices connected via a communication medium like the internet.Computer network development has proposed how humans and devices communicate today.These networks have improved,facilitated,and made conventional forms of communication easier.However,it has also led to uptick in-network threats and assaults.In 2022,the global market for information technology is expected to reach$170.4 billion.However,in contrast,95%of cyber security threats globally are caused by human action.These networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental monitoring.The proposed literature review presents a wide range of information on Wireless Social Networks(WSNs)and Internet of Things(IoT)frameworks.The aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend them.The second is to review the novel work in the domain and find its limitations.The goal is to identify the area’s primary gray field or current research divide to enable others to address the range.Finally,we concluded that configuration.Message Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)forms.The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component.In this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.展开更多
文摘Recommendation services become an essential and hot research topic for researchers nowadays.Social data such asReviews play an important role in the recommendation of the products.Improvement was achieved by deep learning approaches for capturing user and product information from a short text.However,such previously used approaches do not fairly and efficiently incorporate users’preferences and product characteristics.The proposed novel Hybrid Deep Collaborative Filtering(HDCF)model combines deep learning capabilities and deep interaction modeling with high performance for True Recommendations.To overcome the cold start problem,the new overall rating is generated by aggregating the Deep Multivariate Rating DMR(Votes,Likes,Stars,and Sentiment scores of reviews)from different external data sources because different sites have different rating scores about the same product that make confusion for the user to make a decision,either product is truly popular or not.The proposed novel HDCF model consists of four major modules such as User Product Attention,Deep Collaborative Filtering,Neural Sentiment Classifier,and Deep Multivariate Rating(UPA-DCF+NSC+DMR)to solve the addressed problems.Experimental results demonstrate that our novel model is outperforming state-of-the-art IMDb,Yelp2013,and Yelp2014 datasets for the true top-n recommendation of products using HDCF to increase the accuracy,confidence,and trust of recommendation services.
基金This work is partly supported by the Malaysian Ministry of Education under Research Management Centre,Universiti Putra Malaysia,Putra Grant scheme with High Impact Factor under Grant Number UPM/700-2/1/GPB/2018/9659400.
文摘A computer network can be defined as many computing devices connected via a communication medium like the internet.Computer network development has proposed how humans and devices communicate today.These networks have improved,facilitated,and made conventional forms of communication easier.However,it has also led to uptick in-network threats and assaults.In 2022,the global market for information technology is expected to reach$170.4 billion.However,in contrast,95%of cyber security threats globally are caused by human action.These networks may be utilized in several control systems,such as home-automation,chemical and physical assault detection,intrusion detection,and environmental monitoring.The proposed literature review presents a wide range of information on Wireless Social Networks(WSNs)and Internet of Things(IoT)frameworks.The aim is first to be aware of the existing issues(issues with traditional methods)and network attacks on WSN and IoT systems and how to defend them.The second is to review the novel work in the domain and find its limitations.The goal is to identify the area’s primary gray field or current research divide to enable others to address the range.Finally,we concluded that configuration.Message Rapid Spanning Tree Protocol(RSTP)messages have higher efficiency in network performance degradation than alternative Bridge Data Unit Protocol(BPDU)forms.The research divides our future research into solutions and newly developed techniques that can assist in completing the lacking component.In this research,we have selected articles from 2015 to 2021 to provide users with a comprehensive literature overview.
