scholarly journals IMPACT ANALYSIS OF PROFILE INJECTION ATTACKS IN RECOMMENDER SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 472-478
Author(s):  
M Ashish Kumar, Yudhvir Singh, Vikas Siwach, Harkesh Sehrawat
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Impact Analysis ◽  
Service Providers ◽  
Data Set ◽  
Injection Attacks ◽  
Attack Scenario ◽  
The Right ◽  
The Impact

Recommender systems are the backbone of all the prediction-based service platforms e.g. Facebook, Amazon, LinkedIn etc. Even companies now a days are using the recommender systems to show users personalized ads. These service providers capture the right audience for their services/ products and hence, improve overall sales. Social networking platforms are using recommender systems for connecting people of similar interests which is almost impossible without recommender systems.  Collaborative filtering-based recommender system is most widely used recommender system. It is used in this research to predict the rating for a specific movie. Accuracy of the prediction define the performance of the overall system. The quality of predictions is degraded by the attackers by injection of fake profiles. In this paper, the various types of profile injection attacks are explained and the attack scenario gets extended to measure the performance of these attacks. Empirical results on the real world publicly available data set shows that these attacks are highly vulnerable. The impact of these attacks in several conditions has been measured and it is tried to find the scenarios where these attacks are more powerful.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Evaluasi Kebijakan Revitalisasi Pasar Tradisional : Studi Pasar Akau Potong Lembu Kota Tanjungpinang

2020 ◽  
Vol 6 (3) ◽  
pp. 294-309
Author(s):  
Jamhur Poti ◽  
Mahadiansar Mahadiansar
Keyword(s):  
Public Space ◽  
Impact Analysis ◽  
Regional Government ◽  
Traditional Markets ◽  
Evaluation Approach ◽  
Library Research ◽  
Physical Form ◽  
Before And After ◽  
The Impact

Revitalizing traditional markets is a form of improving the quality of public space as a policy of the regional government in cooperation with local communities. The purpose of revitalizing traditional markets is not merely to improve the physical form of traditional markets but also to manage these traditional markets. The researcher raised a case study on the policy after revitalizing the Lembu Market, Tanjungpinang City, to what extent the policies that have been implemented before and after the market revitalization match the public's expectations, it is necessary to evaluate the programs that have been implemented. Researchers used policy evaluation techniques with a formal evaluation approach using Dunn 2018 theory. The research method used by researchers used library research, by carrying out a search of several library sources such as e-books, journals, websites, organizational reports, and other good documents. print and online relevant to the topic being evaluated. The results showed that the evaluation of the revitalization program for the beef slaughter market in the city of Tanjungpinang had not found the value of cross-impact analysis and discounting on the program so that the revitalization of traditional markets was only in the form of target mapping, value clarification and mapping of barriers that had become the impact of the revitalization policy of the traditional cattle slaughter market in Tanjungpinang City. The researcher also did not find Urgency in realizing the traditional market revitalization policy in order to change the characteristics of the market for the better.

scholarly journals Corporate Governance Role in Improving the Financial Information Quality within the Saudi Electricity Company: دور حوكمة الشركات في تحسين جودة المعلومات المحاسبة في الشركة السعودية للكهرباء

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Arwa Hassan Baabbad
Keyword(s):  
Corporate Governance ◽  
Significant Relationship ◽  
Information Quality ◽  
Survey Methodology ◽  
Governance System ◽  
And Performance ◽  
The Right ◽  
Descriptive Survey ◽  
The Impact

The present study aimed to find out the role of corporate governance in improving the quality of information in the Saudi Electricity Company. The researcher used the descriptive survey methodology. As to achieve the study objectives، the researcher utilized the questionnaire tool، in which the study sample (50) members of SEC distributed into employees، managers and decision makers. The study concluded to many results، among of which are: there is a statistically significant relationship between the availability of corporate governance system and performance improvement of the Saudi Electricity Company، there is a statistically significant relationship between corporate governance and appropriateness in improving the performance of the Saudi Electricity Company، it was also found that there is a statistically significant relationship between corporate governance and optimal disclosure in improving the performance of Saudi Electricity Company. The study also found that there is a statistically significant relationship between corporate governance and the right timing in improving the performance of the Saudi Electricity Company. The study suggested number of recommendations، among of which are: the importance of the shareholding companies to comply with the corporate governance regulations considering the interest of companies and their shareholders and all other parties benefiting from the financial statements، attempting to take advantage of the multiple benefits of corporate governance and expand its application in the various economic units in Saudi Arabia، conduct studies on companies that applies the requirement of the Corporate Governance Regulations، and the impact of the application of corporate governance on the shares of these units to find out the relationship between the quality of accounting information in light of the application of corporate governance and the stock market from another angle، imposing deterrent penalties concerning the Corporate Governance Regulations on companies that did not apply this regulation.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

scholarly journals Tentative Exploration of Bacterial Concrete with Partial Replacement of Stone Dust

2019 ◽  
Vol 8 (4) ◽  
pp. 3610-3615
Keyword(s):  
Liberal Arts ◽  
Positive Impact ◽  
Partial Replacement ◽  
X Ray Diffraction ◽  
Stone Dust ◽  
Scale Breaking ◽  
The Right ◽  
The Impact ◽  
3D Shapes

Concrete remains the foremost magnificently used artifact on the soil. The vulnerability of concrete to very little scale breaking decreases its quality and toughness. The repair steel onself for splits is pricey, time disbursement and gets to be arduous in blocked off regions. A self mending bioremediation procedure consolidating spar quick microorganism has been projected among the show take into consideration which could operate the arrangement to this issue. The being spar precipitation will naturally recuperate the smaller scale splits and pores inside the concrete and avoid the broadening of splits. this could cause the thrifty of costs and times went through for maintenance and avoid the subsequent misfortune in quality and solidness of concrete. The concentration of true bacteria Megatherium 10^5cfu/ml and salt were introduced in auxiliary concrete to comprehend the right concentration of microbes. the quality of the foremost elevated review of being concrete had progressed as compared to the foremost reduced review due to the classification of the component. The cogitate has been created to assess the impact on mechanical and toughness properties of M35 review concrete created with substitution of cement with quarry clean ( third,15%,20% and, twenty five and 30%) for each set mechanical property wear examined by liberal arts compression check for 3d shapes, flexural check for bars at that quality ar progressing to be reached thereon result able to embrace the true bacteria Megaterum and at the instant another time discover out the compression take a look at for 3d shapes, flexural check for bars. being spar precipitation was evaluated utilizing associate X-ray diffraction investigation visualized by filtering research and analyzed by vitality dispersive spectroscope. it had been found that the right concentration of B.megaterium had a positive impact on quality auxiliary concrete.

scholarly journals Impact Analysis of Parking Price Adjustment on the Quality of Service of Airport Parking Lots for Light Vehicles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Cheng Cheng ◽  
Peng Qi
Keyword(s):  
Quality Of Service ◽  
Impact Analysis ◽  
Demand Management ◽  
Evaluation Framework ◽  
Daily Maximum ◽  
Before And After ◽  
Parking Lot ◽  
The Impact ◽  
Parking Demand

Pricing is a common measure for parking demand management that has been implemented worldwide. However, the impact of parking price on a parking lot’s quality of service is seldom discussed. This study investigated the impacts of a ladder daily maximum fee charging strategy on the quality of service of the Hongqiao International Airport parking lot based on automatic transaction data before and after the strategy was implemented. An evaluation framework considering managers’ and users’ perspectives was designed. The estimation results show that the new price regulation method largely discouraged long-term parking demand and improved the availability of airport parking facilities, especially during long holidays. As a consequence, throughput and income largely increased in the airport, and there were extra time costs during vehicle departures. The price elasticity of parkers with different parking durations was further estimated. The results showed that price sensitivity is relatively inelastic but varies based on parking duration.

scholarly journals Influence of the weights in IHS and Brovey methods for pan-sharpening WorldView-3 satellite images

2017 ◽  
Vol 6 (3) ◽  
pp. 71 ◽  
Author(s):  
Claudio Parente ◽  
Massimiliano Pepe
Keyword(s):  
Satellite Images ◽  
Urban Landscape ◽  
Spectral Response ◽  
Rural Landscape ◽  
Spectral Radiance ◽  
Data Sets ◽  
Data Set ◽  
Inertial Moment ◽  
The Impact

The purpose of this paper is to investigate the impact of weights in pan-sharpening methods applied to satellite images. Indeed, different data sets of weights have been considered and compared in the IHS and Brovey methods. The first dataset contains the same weight for each band while the second takes in account the weighs obtained by spectral radiance response; these two data sets are most common in pan-sharpening application. The third data set is resulting by a new method. It consists to compute the inertial moment of first order of each band taking in account the spectral response. For testing the impact of the weights of the different data sets, WorlView-3 satellite images have been considered. In particular, two different scenes (the first in urban landscape, the latter in rural landscape) have been investigated. The quality of pan-sharpened images has been analysed by three different quality indexes: Root mean square error (RMSE), Relative average spectral error (RASE) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS).

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

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