scholarly journals Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Xuening Chen ◽  
Hanwen Liu ◽  
Yanwei Xu ◽  
Chao Yan
Keyword(s):  
Service Quality ◽  
Privacy Preservation ◽  
False Negative ◽  
Locality Sensitive Hashing ◽  
Quality Data ◽  
Service Recommendation ◽  
User Privacy ◽  
Target User ◽  
Cross Platform ◽  
Hybrid Collaborative Filtering

Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

scholarly journals Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Can Zhang ◽  
Junhua Wu ◽  
Chao Yan ◽  
Guangshun Li
Keyword(s):  
Real World ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Locality Sensitive Hashing ◽  
Service Recommendation ◽  
Cross Platform ◽  
Time Aware ◽  
Temporal Feature

IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

scholarly journals Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yanwei Xu ◽  
Lianyong Qi ◽  
Wanchun Dou ◽  
Jiguo Yu
Keyword(s):  
Privacy Preservation ◽  
Service Selection ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Service Recommendation ◽  
Service Usage ◽  
Target User ◽  
Usage Data

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

scholarly journals “Time–Location–Frequency”–aware Internet of things service selection based on historical records

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668869 ◽  
Author(s):  
Lianyong Qi ◽  
Peiqiang Dai ◽  
Jiguo Yu ◽  
Zhili Zhou ◽  
Yanwei Xu
Keyword(s):  
Internet Of Things ◽  
Service Quality ◽  
Dynamic Network ◽  
Service Selection ◽  
Historical Record ◽  
Quality Data ◽  
Historical Records ◽  
Data Set ◽  
Target User ◽  
Selection Approach

The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach “Time–Location–Frequency”–aware Service Selection Approach is put forward in this article. In Time–Location–Frequency–aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM, we validate the feasibility of our proposal.

PENGARUH KUALITAS PELAYANAN DAN WORD OF MOUTH TERHADAP KEPUTUSAN PEMBELIAN DI KAFE POTRET MEDAN

2019 ◽  
Vol 13 (2) ◽  
pp. 199
Author(s):  
Asrye Tutur Sinaga ◽  
Nurul Wardani
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Service Quality ◽  
Word Of Mouth ◽  
Hypothesis Test ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Quality Data ◽  
Purchasing Decisions ◽  
Study Hypothesis

AbstrakPenelitian ini bertujuan untuk mengetahui dan dapat menjelaskan pengaruh Kualitas Pelayanan dan Word Of Mouth terhadap Keputusan pembelian di Kafe Potret Medan. Populasi dalam penelitian ini adalah 700 orang ditentukan dari jumlah pengunjung Kafe Potret Medan dalam kurun waktu satu minggu, dan sampel yang digunakan berjumlah 88 pengunjung. Sedangkan tehnik pengumpulan data menggunakan angket (kuesioner) dan pengujiannya yaitu uji kualitas data dan uji asumsi klasik.Pengujian hipotesis menggunakan analisis regresi linier berganda, uji F, uji t, dan uji R2. Hipotesis penelitian dimensi Kualitas Pelayanan dan Word Of Mouth secara parsial terhadap Keputusan Pembelian diterima jika t hitung > t tabel dengan tingkat signifikan 0.05.Nilai t tabel dalam penelitian ini 1,662. Nilai t hitung variabel X1 sebesar 1,990 t hitung  > t tabel maka hipotesis diterima, nilai t hitung variabel X2 sebesar 2,628 t hitung > t tabel maka hipotesis diterima. Dari 2 variabel, variabel Word Of Mouth yang paling dominan mempengaruhi Keputusan Pembelian  sebesar 2,628. Kata Kunci : Kualitas Pelayanan, Word Of Mouth, Keputusan Pembelian AbstractThe purpose of this study is to identify and able to explain the influence of Service Quality and Word Of Mouth to Purchasing Decisions of Kafe Potret Medan. The population in this study were 700 people from visitors Kafe Potret Medan in one week, and the samples used were 88 visitors. While the techniques of data collection using the questionnaire and use the test of quality data and classical assumption. The hypothesis test uses multiple linear regression analysis, F test, R square and t test. The study hypothesis was partially of Service Quality and Word Of Mouth dimension to  Purchasing Decisions is acceptable if t hitung > t tabel with a significant level 0.05. The t tabel value in this study 1.662. The t hitung X1 is 1.990 that mean t hitung > t tabel then the hypothesis is accepted, t hitung X2 is 2.628 that mean t hitung > t tabel then the hypothesis is accepted. From 2 variables fascination that the most dominant variable for Purchasing Decisions is Word Of Mouth of 2.628. Keywords : Service Quality, Word Of Mouth, Purchasing Decisions

Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing

2019 ◽  
Vol 23 (5) ◽  
pp. 1167-1185
Author(s):  
Xiaohan Wang ◽  
Yonglong Luo ◽  
Shiyang Liu ◽  
Taochun Wang ◽  
Huihui Han
Keyword(s):  
Privacy Preservation ◽  
Location Privacy ◽  
Locality Sensitive Hashing

scholarly journals A New Application-Aware No-Reference Quality Assessment Method for IP Voice Services

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Péter Orosz ◽  
Tamás Tóthfalusi
Keyword(s):  
Service Quality ◽  
Performance Metrics ◽  
Assessment Method ◽  
Reference Source ◽  
User Privacy ◽  
Quality Model ◽  
Estimation Model ◽  
Full Reference ◽  
Reference Quality ◽  
The Voice

AbstractThe increasing number of Voice over LTE deployments and IP-based voice services raise the demand for their user-centric service quality monitoring. This domain’s leading challenge is measuring user experience quality reliably without performing subjective assessments or applying the standard full-reference objective models. While the former is time- and resource-consuming and primarily executed ad-hoc, the latter depends upon a reference source and processes the voice payload that may offend user privacy. This paper presents a packet-level measurement method (introducing a novel metric set) to objectively assess network and service quality online. It is accomplished without inspecting the voice payload and needing the reference voice sample. The proposal has three contributions: (i) our method focuses on the timeliness of the media traffic. It introduces new performance metrics that describe and measure the service’s time-domain behavior from the voice application viewpoint. (ii) Based on the proposed metrics, we also present a no-reference Quality of Experience (QoE) estimation model. (iii) Additionally, we propose a new method to identify the pace of the speech (slow or dynamic) as long as voice activity detection (VAD) is present between the endpoints. This identification supports the introduced quality model to estimate the perceived quality with higher accuracy. The performance of the proposed model is validated against a full-reference voice quality estimation model called AQuA, using real VoIP traffic (originated in assorted voice samples) in controlled transmission scenarios.

Using Chernoff faces to portray service quality data

1994 ◽  
Vol 10 (1-3) ◽  
pp. 247-255 ◽  
Author(s):  
Deon Nel ◽  
Leyland Pitt ◽  
Trevor Webb
Keyword(s):  
Service Quality ◽  
Quality Data
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