Submit Search
Upload
Travel Recommendation Approach using Collaboration Filter in Social Networking
•
0 likes
•
35 views
IRJET Journal
Follow
https://irjet.net/archives/V4/i8/IRJET-V4I8220.pdf
Read less
Read more
Engineering
Report
Share
Report
Share
1 of 8
Download now
Download to read offline
Recommended
Location Privacy Protection Mechanisms using Order-Retrievable Encryption for...
Location Privacy Protection Mechanisms using Order-Retrievable Encryption for...
IRJET Journal
Socially augmented software empowering software operation through social cont...
Socially augmented software empowering software operation through social cont...
Engineering and Social Informatics (ESOTICS)
2000-ACM-SigMobile-Mobile computing and communications review - Marc Smith - ...
2000-ACM-SigMobile-Mobile computing and communications review - Marc Smith - ...
Marc Smith
Survey of data mining techniques for social
Survey of data mining techniques for social
Firas Husseini
2053951715611145
2053951715611145
Firas Husseini
Jf2516311637
Jf2516311637
IJERA Editor
Information Architecture & Findability
Information Architecture & Findability
Are Halland
CS6010 Social Network Analysis Unit IV
CS6010 Social Network Analysis Unit IV
pkaviya
Recommended
Location Privacy Protection Mechanisms using Order-Retrievable Encryption for...
Location Privacy Protection Mechanisms using Order-Retrievable Encryption for...
IRJET Journal
Socially augmented software empowering software operation through social cont...
Socially augmented software empowering software operation through social cont...
Engineering and Social Informatics (ESOTICS)
2000-ACM-SigMobile-Mobile computing and communications review - Marc Smith - ...
2000-ACM-SigMobile-Mobile computing and communications review - Marc Smith - ...
Marc Smith
Survey of data mining techniques for social
Survey of data mining techniques for social
Firas Husseini
2053951715611145
2053951715611145
Firas Husseini
Jf2516311637
Jf2516311637
IJERA Editor
Information Architecture & Findability
Information Architecture & Findability
Are Halland
CS6010 Social Network Analysis Unit IV
CS6010 Social Network Analysis Unit IV
pkaviya
Knowing your public
Knowing your public
Firas Husseini
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
IJCI JOURNAL
Published Paper
Published Paper
Faeza Noor
A42020106
A42020106
IJERA Editor
Data mining in social network
Data mining in social network
akash_mishra
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
inventionjournals
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
ijcsit
Online social network
Online social network
ingenioustech
Analysis, modelling and protection of online private data.
Analysis, modelling and protection of online private data.
Silvia Puglisi
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
IRJET Journal
nm
nm
Zainab Nayyar
LyonALMProposal20041018.doc
LyonALMProposal20041018.doc
butest
Social Network Analysis (SNA)
Social Network Analysis (SNA)
Development Innovations
Social media mining PPT
Social media mining PPT
Chhavi Mathur
Social Data Mining
Social Data Mining
Mahesh Meniya
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Sophia Guevara
Benchmarking the Privacy-Preserving People Search
Benchmarking the Privacy-Preserving People Search
Daqing He
Netnography
Netnography
Raj Shravanthi
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
paperpublications3
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
IRJET Journal
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
csandit
Predicting Venues in Location Based Social Network
Predicting Venues in Location Based Social Network
cscpconf
More Related Content
What's hot
Knowing your public
Knowing your public
Firas Husseini
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
IJCI JOURNAL
Published Paper
Published Paper
Faeza Noor
A42020106
A42020106
IJERA Editor
Data mining in social network
Data mining in social network
akash_mishra
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
inventionjournals
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
ijcsit
Online social network
Online social network
ingenioustech
Analysis, modelling and protection of online private data.
Analysis, modelling and protection of online private data.
Silvia Puglisi
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
IRJET Journal
nm
nm
Zainab Nayyar
LyonALMProposal20041018.doc
LyonALMProposal20041018.doc
butest
Social Network Analysis (SNA)
Social Network Analysis (SNA)
Development Innovations
Social media mining PPT
Social media mining PPT
Chhavi Mathur
Social Data Mining
Social Data Mining
Mahesh Meniya
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Sophia Guevara
Benchmarking the Privacy-Preserving People Search
Benchmarking the Privacy-Preserving People Search
Daqing He
Netnography
Netnography
Raj Shravanthi
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
paperpublications3
What's hot
(19)
Knowing your public
Knowing your public
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
SEMANTIC WEB: INFORMATION RETRIEVAL FROM WORLD WIDE WEB
Published Paper
Published Paper
A42020106
A42020106
Data mining in social network
Data mining in social network
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
Multi-Mode Conceptual Clustering Algorithm Based Social Group Identification ...
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
APPLYING THE TECHNOLOGY ACCEPTANCE MODEL TO UNDERSTAND SOCIAL NETWORKING
Online social network
Online social network
Analysis, modelling and protection of online private data.
Analysis, modelling and protection of online private data.
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
A Survey on Trust Inference Network for Personalized Use from Online Data Rating
nm
nm
LyonALMProposal20041018.doc
LyonALMProposal20041018.doc
Social Network Analysis (SNA)
Social Network Analysis (SNA)
Social media mining PPT
Social media mining PPT
Social Data Mining
Social Data Mining
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Social Network Analysis and Partnerships SNA presentation Guevara 2015
Benchmarking the Privacy-Preserving People Search
Benchmarking the Privacy-Preserving People Search
Netnography
Netnography
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Similar to Travel Recommendation Approach using Collaboration Filter in Social Networking
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
IRJET Journal
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
csandit
Predicting Venues in Location Based Social Network
Predicting Venues in Location Based Social Network
cscpconf
Paper id 41201614
Paper id 41201614
IJRAT
IRJET- Next Location Prediction
IRJET- Next Location Prediction
IRJET Journal
Recommendation System Using Social Networking
Recommendation System Using Social Networking
ijcseit
IRJET- Cloud-Based Video Propositions with Classification of Client in Public...
IRJET- Cloud-Based Video Propositions with Classification of Client in Public...
IRJET Journal
Situation Alert and Quality of Service using Collaborative Filtering for Web ...
Situation Alert and Quality of Service using Collaborative Filtering for Web ...
IRJET Journal
Service Rating Prediction by check-in and check-out behavior of user and POI
Service Rating Prediction by check-in and check-out behavior of user and POI
IRJET Journal
IRJET- Shopping Mall Experience using Beacon Technology
IRJET- Shopping Mall Experience using Beacon Technology
IRJET Journal
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
IRJET Journal
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Editor IJAIEM
Service rating prediction by exploring social mobile users’ geographical loca...
Service rating prediction by exploring social mobile users’ geographical loca...
CloudTechnologies
Ijsrdv7 i10842
Ijsrdv7 i10842
aissmsblogs
Following the user’s interests in mobile context aware recommender systems
Following the user’s interests in mobile context aware recommender systems
Bouneffouf Djallel
E0341021025
E0341021025
inventionjournals
A Survey of Building Robust Business Models in Pervasive Computing
A Survey of Building Robust Business Models in Pervasive Computing
Osama M. Khaled
Optimized travel recommendation using location based collaborative filtering
Optimized travel recommendation using location based collaborative filtering
IRJET Journal
IRJET- Popularity based Recommender Sytsem for Google Maps
IRJET- Popularity based Recommender Sytsem for Google Maps
IRJET Journal
Application Of Android Enabled Mobile Device For Personal Information Systems
Application Of Android Enabled Mobile Device For Personal Information Systems
ijasa
Similar to Travel Recommendation Approach using Collaboration Filter in Social Networking
(20)
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
Projection Multi Scale Hashing Keyword Search in Multidimensional Datasets
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
Predicting Venues in Location Based Social Network
Predicting Venues in Location Based Social Network
Paper id 41201614
Paper id 41201614
IRJET- Next Location Prediction
IRJET- Next Location Prediction
Recommendation System Using Social Networking
Recommendation System Using Social Networking
IRJET- Cloud-Based Video Propositions with Classification of Client in Public...
IRJET- Cloud-Based Video Propositions with Classification of Client in Public...
Situation Alert and Quality of Service using Collaborative Filtering for Web ...
Situation Alert and Quality of Service using Collaborative Filtering for Web ...
Service Rating Prediction by check-in and check-out behavior of user and POI
Service Rating Prediction by check-in and check-out behavior of user and POI
IRJET- Shopping Mall Experience using Beacon Technology
IRJET- Shopping Mall Experience using Beacon Technology
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Service rating prediction by exploring social mobile users’ geographical loca...
Service rating prediction by exploring social mobile users’ geographical loca...
Ijsrdv7 i10842
Ijsrdv7 i10842
Following the user’s interests in mobile context aware recommender systems
Following the user’s interests in mobile context aware recommender systems
E0341021025
E0341021025
A Survey of Building Robust Business Models in Pervasive Computing
A Survey of Building Robust Business Models in Pervasive Computing
Optimized travel recommendation using location based collaborative filtering
Optimized travel recommendation using location based collaborative filtering
IRJET- Popularity based Recommender Sytsem for Google Maps
IRJET- Popularity based Recommender Sytsem for Google Maps
Application Of Android Enabled Mobile Device For Personal Information Systems
Application Of Android Enabled Mobile Device For Personal Information Systems
More from IRJET Journal
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
IRJET Journal
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
IRJET Journal
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
IRJET Journal
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
IRJET Journal
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
IRJET Journal
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
IRJET Journal
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
IRJET Journal
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
IRJET Journal
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
IRJET Journal
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
IRJET Journal
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
IRJET Journal
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
IRJET Journal
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
IRJET Journal
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
IRJET Journal
React based fullstack edtech web application
React based fullstack edtech web application
IRJET Journal
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
IRJET Journal
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
IRJET Journal
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
IRJET Journal
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
IRJET Journal
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
IRJET Journal
More from IRJET Journal
(20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTURE
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...
Effect of Camber and Angles of Attack on Airfoil Characteristics
Effect of Camber and Angles of Attack on Airfoil Characteristics
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...
A REVIEW ON MACHINE LEARNING IN ADAS
A REVIEW ON MACHINE LEARNING IN ADAS
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
P.E.B. Framed Structure Design and Analysis Using STAAD Pro
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...
Survey Paper on Cloud-Based Secured Healthcare System
Survey Paper on Cloud-Based Secured Healthcare System
Review on studies and research on widening of existing concrete bridges
Review on studies and research on widening of existing concrete bridges
React based fullstack edtech web application
React based fullstack edtech web application
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Multistoried and Multi Bay Steel Building Frame by using Seismic Design
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...
Recently uploaded
Software Development Life Cycle By Team Orange (Dept. of Pharmacy)
Software Development Life Cycle By Team Orange (Dept. of Pharmacy)
Suman Mia
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
SIVASHANKAR N
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptx
purnimasatapathy1234
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
João Esperancinha
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
Suhani Kapoor
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
slot gacor bisa pakai pulsa
Introduction to IEEE STANDARDS and its different types.pptx
Introduction to IEEE STANDARDS and its different types.pptx
upamatechverse
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
Call Girls in Nagpur High Profile
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
hassan khalil
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
rehmti665
Introduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptx
upamatechverse
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Dr.Costas Sachpazis
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
null - The Open Security Community
Extrusion Processes and Their Limitations
Extrusion Processes and Their Limitations
120cr0395
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
RajaP95
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
ranjana rawat
Roadmap to Membership of RICS - Pathways and Routes
Roadmap to Membership of RICS - Pathways and Routes
M Maged Hegazy, LLM, MBA, CCP, P3O
Processing & Properties of Floor and Wall Tiles.pptx
Processing & Properties of Floor and Wall Tiles.pptx
pranjaldaimarysona
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
srsj9000
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Low Rate Call Girls In Saket, Delhi NCR
Recently uploaded
(20)
Software Development Life Cycle By Team Orange (Dept. of Pharmacy)
Software Development Life Cycle By Team Orange (Dept. of Pharmacy)
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
MANUFACTURING PROCESS-II UNIT-2 LATHE MACHINE
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
VIP Call Girls Service Kondapur Hyderabad Call +91-8250192130
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
DJARUM4D - SLOT GACOR ONLINE | SLOT DEMO ONLINE
Introduction to IEEE STANDARDS and its different types.pptx
Introduction to IEEE STANDARDS and its different types.pptx
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Meera Call 7001035870 Meet With Nagpur Escorts
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Introduction to Multiple Access Protocol.pptx
Introduction to Multiple Access Protocol.pptx
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Sheet Pile Wall Design and Construction: A Practical Guide for Civil Engineer...
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Exploring_Network_Security_with_JA3_by_Rakesh Seal.pptx
Extrusion Processes and Their Limitations
Extrusion Processes and Their Limitations
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
(SHREYA) Chakan Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Esc...
Roadmap to Membership of RICS - Pathways and Routes
Roadmap to Membership of RICS - Pathways and Routes
Processing & Properties of Floor and Wall Tiles.pptx
Processing & Properties of Floor and Wall Tiles.pptx
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
Travel Recommendation Approach using Collaboration Filter in Social Networking
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1233 TRAVEL RECOMMENDATION APPROACH USING COLLABORATION FILTER IN SOCIAL NETWORKING *1Mr. Prabu S., *2 Mrs. K. R. Dillirani. *1M.Phil Research Scholar, PG & Research Department of Computer Science & Information Technology, Dr M G R Chockalingam College of Arts and Science, Arni, Tamil Nadu, India. *2.Assistant Professor, PG & Research Department of Computer Science & Information Technology, Dr M G R Chockalingam College of Arts and Science, Arni, Tamil Nadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract: Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user’s travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community- contributed photos. We map both user’s and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and session). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7, 387 users and 24, 008 travelogues covering 864 travel POIs in 9 famous cities, and show its effectiveness. We also contribute a new dataset with more than 200K photos with heterogeneous metadata in 9 famous cities. Keywords: Location privacy, location sharing services, location-based social networking, spatio- temporal query processing, Collaboration Filtering, Travel Recommendation, I. INTRODUCTION Recently people have been receiving more and more digitized information from Internet, and the volume of information is larger than any other point in time, reaching a point of information overload. To solve this problem, the recommender system has been created in response to the need to disseminate so much information. It does not only filter the noise, but also help to select attractive and useful information. Recommender system has achieved initial success based on a survey that shows at least 20 percent of sales on Amazon’s website come from the recommender system.Social networks gather volumes of information contributed by users around the world. This information is versatile. It always contains item/services descriptions (including textual descriptions, logos and pictures), users’ comments, moods and users’ social circles, prices, and locations. It is very popular for recommending users’ favorite services from crowd-source contributed information. However, with the rapid increase in number of registered Internet users and more and more new products available for purchase online, the issue of cold start for users and sparsity of datasets has become increasingly intractable. Fortunately, with the popularity and rapid development of social networks, more and more users enjoy sharing their experiences, such as reviews, ratings, photos and moods. The interpersonal relationships have become transparent and opened up as more and more users share this information on social media websites such as Facebook, Twitter, Yelp, Douban, Epinions, etc. The circles of friends also bring opportunities and challenges for a recommender system to solve the issues of cold start and sparsity. Fig : Location based Services For the scope of this thesis we focus on Location- Based Service (LBS). In these services the user's location is processed in order to deliver a service, such as finding a nearby Point-of-Interest (POI) or getting directions. Due to
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1234 their usefulness, LBS are being used by most mobile device users. Some services require the users to reveal their location either sporadically or continuously. The difference between the two is that in the sporadic case two consecutive queries to the LBS of the same user happen with sufficient time in between so that they do not correlate. We shall understand LBSs from a broader prospect than only from a technology perspective. Instead, we consider processing location data as a new socio- technical practice, having impact on technology, society and legislation. We adopt an interdisciplinary approach in order to understand the threats on privacy from engineering, ethical and legal perspectives. For the domain of obfuscation-based location privacy protection we first propose a framework that allows the computation of optimal LPPMs for users who sporadically engage in LBSs. Our framework accounts for resource constraints of mobile devices with which LBSs are typically being accessed. This makes our framework suitable to compute optimal dummy and precision-based obfuscation strategies. An analysis of several optimal obfuscation strategies further shows the trilateral trade- off of privacy, Quality Loss (QL) and bandwidth overhead A concept of the rating schedule to represent user daily rating behavior. The factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. The main factors are, personal interest, interpersonal interest similarity, interpersonal rating behavior similarity, and interpersonal rating behavior diffusion, into matrix factorization with fully exploring user rating behaviors to predict user-service ratings. II. BACK GROUND Privacy assessment of LBSs from engineering, an ethical and a legal perspective. Scholars from every of the involved disciplines obtain an added value for their own field and additionally obtain an insight in how the topic is addressed in other disciplines. For example, the detail provided by the legal perspective serves as a valuable reference for legal scholars. However, scholars from other disciplines may find the provided information especially valuable, because works in their discipline typically lack this kind of legal detail. Also, the combination of the engineering, ethical and legal discipline to assess privacy implications in LBS contributes towards a better understanding of privacy issues from a broader perspective. It apply the CI heuristic in a novel way. Typically, the CI heuristic is used to analyze how a new socio- technical practice impacts the CI in very specific situations. For example, consider a library that provides a paper- based repository of all available books that can be used by visitors to locate the books in which they are interested. If the library would replace this paper-based system by a computer-based search system, then the situation for the visitors would fundamentally change. While consulting the paper-based repository leaves no digital trace, any action on the computer-based system can be recorded. Finding the prevailing context of the CI heuristic is in this scenario relatively straight forward, because visitors use the system only in the library. This is different with LBSs as they are employed in numerous situations and contexts. In our work we analyze the impact of the new socio-technical practice on CI independent of a particular scenario. Instead, we employ the context of travelling for our analysis. Location Data It presents the necessary background on location data. We provide a detailed explanation on apps that are the most important tools for people to utilize the capabilities of their mobile devices, such as their location data. This also includes a description of the prevailing business model of the app-eco system and its privacy issues, as well as the users' attitude towards this business model. We continue with the explanation of the legal protection of location data and outline users' attitude towards LBSs. Finally, we provide a summary of the threats to user privacy when location data is misused. Mobile Device Eco-System Mobile devices are nowadays ubiquitous companions. More than two billion people worldwide are using smartphones and more than 1.2 billion people are using tablet computers. Smartphones are intuitive to use and they are equipped with a wide variety of communication modules, such as WiFi, Bluetooth and 4G/LTE, allowing their users to be connected in ways never possible before. Another reason for the success of smartphones is their platform, i.e. the Mobile Operating System (MOS) and the app-store that are maintained by the Platform Operator (PO). More traditional mobile phones usually run a MOS developed by the phone manufacturer and offered only a very limited number of additional applications, which typically have also been implemented by the phone manufacturer. The software of modern mobile devices, however, is much more sophisticated. Legal Protection of Location Data There are several directives in place for the protection of location data of which we discuss in detail the DPD, the ePrivacy Directive (2002/58/EC, as amended by 2009/136/EC) (ePrivacy Directive) directive and the
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1235 recently published GDPR. The Data Protection Directive (95/46/EC) (DPD) [190] defines the legal rules for processing personal data. The term processing is very broad and can be anything from recording, handling and deleting data. The term personal data refers to "any information relating to an identified or identifiable natural person (data subject)", whereas the identification can be direct or indirect. Direct means to identify an individual without third party data sourcesMany models based on social networks have been proposed to improve recommender system performance. The concept of ‘inferred trust circle’ based on circles of friends. To recommend favorite and useful items to users. Their approach, called the Circle on Model, not only reduces the load of big data and computation complexity, but also defines the interpersonal trust in the complex social networks. The similarity between users or items according to the number of common ratings. An item-based CF combined with a condition-based probability similarity and Cosine Similarity. Collaborative filtering-based recommendation approaches can be viewed as the first generation of recommender system. III. DESIGN OF PRIVATE LOCATION-BASED Most LBSs for mobile devices are privacy invasive, because the service provider learns the user's location data. A variety of services have been proposed that make this privacy invasion impossible. Such services employ cryptographic primitives that allow them to provide the necessary privacy guarantees. One of the most commonly used cryptographic primitives is homomorphic encryption that allows computations to be carried out on ciphertext. There exists several cryptosystems that provide this homomorphic property. Cryptosystem possess the additive homomorphic property. Given two plaintexts m1 and m2 and their respective encryptions E(m1) and E(m2), the following equation holds: E.(mi) © E.(m2) = E(mi + m2) -----(1) Geo-Social Networks It proposes a private location-sharing service and employs hybrid encryption to protect the user's privacy. Every user is assumed to have a public/private key pair and users can exchange their public keys either out-of- band or with the help of the service provider. If Alice wants to inform Bob about her current location, she chooses a secret key, encrypts her location with the secret key, encrypts the secret key with the public key of Bob and sends both to Bob. The receiver Bob uses his private key to decrypt the symmetric key, enabling him to decrypt Alice's location. The data being sent between Alice and Bob could be transferred with the help of a central service provider, but if the service provider is reluctant to store and forward encrypted data, Alice and Bob could exchange their information with the help of a Distributed Hash Table (DHT), POI Finder One of the most commonly used LBSs allows users to find Point-of-Interest (POI) around their location. Therefore, the user submits a query to the service provider along with her location and, optionally, some additional information on what kind of POIs the user is interested in. Private Information Retrieval (PIR) is suited to implement a POI finder in a privacy-preserving way. PIR is a mechanism that allows users to query a database without the database server learning what information the user requested. It propose two protocols based on PIR. The first protocol works with a single PIR request at the cost of providing only approximate results. The second protocol has a higher computational and communication overhead but provides more accurate query results. Both protocols are built on a data structure based on Hilbert curves and search trees that convert the map coordinates of POIs into 1-dimensional coordinates preserving the proximity of POIs. This allows to apply PIR on originally two-dimensional data. Traffic Monitoring The idea of traffic monitoring is that cars on the road are equipped with tracking devices and report to a LBS additional data, such as their current speed. This allows the LBS to compute statistics, such as current road utilization, that can be used to navigate cars in a more efficient way. Clearly, such a system can provide considerable advantages, including the detection of traffic jams and the subsequent redirection of other cars to a faster route. However, if cars constantly reveal their locations, any observer would be able to learn private information about the users Formally define the Sybil detection problem. Specifically, we first introduce the social Network model. Then we introduce a few design goals.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1236 Fig : The propagation in Sybil Let us consider an undirected social network G = (V;E), where a node v 2 V represents a user in the system and an edge (u; v) 2 E indicates that the users u 2 V and v 2 V are socially connected. In an ideal setting, G represents a weighted network of trust relationships between Users, where the edge weights represent the levels of trust between users [92]. Each node is either benign or Sybil. Figure 3.1 illustrates a Sybil attack. We denote the sub network including the benign nodes and the edges between them as the benign region, denote the sub network including the Sybil’s and edges between them as the Sybil region, and denote the edges between the two regions as attack edges. Note that the benign region could consist of multiple communities and we will evaluate their impact on Sybil detections IV. PROPOSED ANALYSIS In our research, user, item and tag are three fundamental entities of data. For the three basic entities, we define {a/, b/ and T as disjoint non-empty finite sets. We use set symbols in lower case with indices in the subscripts to denote individual elements of a set, for example, u2 denotes the second user in the user set {c/. We use symbols of individual set elements, i.e., user , item iy, or tag tp, in the subscripts or superscripts of the set symbols, as conditions imposed onto the sets to denote subsets. For example, denotes the item set in which each item was used by the vth user u„ 6 U. Users. U = u2, — , u^, — ,U|y|} contains all users in a social tagging system. denotes the vth user, u56 0, 1 <v < |a[a/b|. Items. / = (t1,t2, —, ty, —, t|7|} contains all items used by the users in the system. iy denotes the y'th item, iy 6 /, 1 < _/' < |a/b|. Tags. T = (t1, t2, —, tp, —, t|T|} contains all tags used by the users in the system. tp denotes the pth tag, tp 6 T, 1 < p < |T|. Tag assignments. The basic tagging behaviour, namely tag assignment, is defined as a 3-tuple e: U x / x T 6 {0,1}. If a user collected item t; with tag t„, then e„ t =1, otherwise e„ t =0. User Profiling Based On Multidimensional Singular Value Decomposition Traditionally some two-dimensional CF approaches apply SVD on a user-item matrix to compute user or item profiles and identify similar users/items (Symeonidis et al., 2006; Mi Zhang & Hurley, 2009). In these approaches, user-item matrix M 6 R't/'x'7' is decomposed and approximated by the truncated SVD: Taking user-based CF, for example, <W|[/|XFC 1 £fcxfc is used to project each user's data from an |/|- dimensional space to a k-dimensional space, where k principal components of the data are preserved. Thus the user profile matrix in the SVD-based user profiling is computed as: Recall that we have a social network G = (V, E) of the nodes in the system. Each node can have two states, i.e., benign or Sybil. Thus, we associate a binary random variable * +1g with each node. xv = +1 means that node v is a benign node and xv = �1 indicates that node vis Sybil. In the following, we use xA to represent the set of random variables associated with the nodes in the set A. Moreover, we use _xA to denote the observed values of these random variables. There might exist some prior information about a node v independently from all other nodes in the system. Such prior information could be the content generated by v or its behavior. We model the prior belief of v being benign as follows: ( ) ( ) , Where hv quantities the prior information about v. More specifically, hv> 0 encodes the scenario in which v is more likely to be benign; hv< 0 encodes the opposite scenario; hv = 0 means prior information is not helpful to determine v's state. here now introduce * ( ) + , the set of v's neighbors in the social network, and their respective states _x€v . When these states are known, the probability of v to be benign is modeled as ( ) ( A Pair wise Markov Random Field We find that the probabilistic local rule introduced in the previous section can be applied by modeling the social network as a pair wise Markov Random Field (MRF). A MRF defines a joint probability
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1237 distribution for binary random variables associated with all the nodes in the network. Specifically, a MRF is specified with a node potential for each node v, which incorporates prior knowledge about v, and with an edge potential for each edge (u; v), which represents correlations between u and v. In the context of Sybil detection, we define a node potential _v(xv) for the node v as ( ) { Fig : Markov Random Model Implementation Fig : Order Retrieve Encryption Work Process As mentioned above, user locations (i.e., points) in the database server are always in encrypted form. When an “encrypted” query location of Q1 for a group of friends is received by the database server, the database server should determine for any two friends’ encrypted locations within the group which of them is closer to the encrypted query location. To achieve this, we use our proposed encryption notion ORE for geographical data. An ORE scheme is a symmetric key encryption scheme with two additional functions: one is for generating encrypted query locations and the other one is for the database server to determine which one between two encrypted user locations is closer to an encrypted query location. The scheme is called ORE because the order of the encrypted user locations in terms of their distances from any given encrypted query location can be retrieved. Note that the actual distance information is not retrievable. Algorithm Implementation 1. Rating scale of individual users 2. Popularity of individual items 3. User preferences rui(t)= +bu(t)+bi(t)+qT ipu(t) Parameterizing the model rui(t)= +bu(t)+bi(t)+qT ipu(t) Use functional forms: bu(t)=f(u,t), bi(t)=g(i,t), pu (t)=h(u,t) Need to find adequate f(), g(), h() General guidelines: – Items show slower temporal changes – Users exhibit frequent and sudden changes – Factors –pu(t)– are expensive to model – Gain flexibility by heavily parametering the functions 1. User Rating Behavior Exploration The factors of interpersonal interest similarity W∗u,v and personal interest Q∗u,i proposed in have been proved effective. Thus, in this subsection, we turn to the details of our proposed interpersonal rating behavior similarity and interpersonal rating behavior diffusion. 2. Interpersonal Rating Behavior Similarity: The behavior habit is essential. It could not be separated from temporal information. Thus, we define rating behavior in this paper as what the user has done and when it happened. This kind of behavior presentation arouses us to the curriculum schedule. The schedule arranges which course would we take and when we should go to class.
6.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1238 3. Interpersonal Rating Behavior Diffusion: We consider the factor of social users’ rating behavior diffusions. We explore the diffusion of user rating behavior by combining the scope of user’s social network and the temporal information of rating behaviors. For a user, we split his/her social network into three components, direct friends, mutual friends, and the indirect friends. 4. Impact of Predicted Integer Ratings: The predicted ratings are decimal; we discuss the impact of predicted integer ratings on performance. The ratings user rated are all discrete values ranging from 1 to 5. But the predicted results of matrix factorization model are all decimals. Thus, it is necessary to discuss the impact of integer predicted ratings. We round decimal ratings we predicted into discrete integers. 5. Yelp Dataset: Yelp is a local directory service with social networks and user reviews. It is the largest review site in America. Users rate the businesses, submit comments, communicate experience, etc. It combines local reviews and social networking functionality to create a local online community. Yelp dataset4 contains eight categories, including Active Life, Beauty & Spas, Home Services, Hotels & Travel, Night Life, Pets, Restaurants, and Shopping. LOCAL TRUSTEE SELECTION STRATEGIES For a user u, a local trustee selection strategy essentially computes a score s(v, u) for each friend v of u and then selects my friends with the highest scores as u's trustees. T-Random: As a baseline, T-Random assigns a random number ranging from 0 to 1 as the score s(v, u). T-CF: As was shown by the number of common friends of two users is an informative indicator about the level of trust between them. Thus, one speculation is that a user might select friends with which he or she shares many common friends as trustees. To quantify the security of this speculation, we design the strategy T-CF (i.e.,T- Common Friends), which uses the number of common friends shared by u and his or her friend v as the score ( ) , ( ) ⋂ ( )- However, there are two drawbacks of T-CF. First, the fact that u shares many friends with a popular user v doesn't necessarily mean that u and v have a high level of trust because it is normal for many friends of u to be in v's friend list. Second, if a common friend of u and v is a popular user, then sharing him or her doesn'tnecessarily indicate a high level of trust between u and v. Next, we introduce two strategies to overcome the two drawbacks, respectively. T-JC: To overcome the first drawback of T-CF, we design the trustee selection strategy T-JC (i.e., T-Jacquard Coefficient), which uses the Jacquard Coefficient [61] of the two sets T(u) and T(v) as the score ( ) i.e., ( ) ( ) ( ) ( ) ( ) ( ) . …(2) T-AA: To overcome the second drawback, we design the T- AA (i.e., T-Adamic Ada) strategy, which uses Adamic-Ada similarity between u and v as the score s(v,u). Adamic- Ada similarity penalizes each common friend by its popularity (i.e., the number of friends). Formally, we have ( ) ( ) ( ) ( ) T-Degree:, users could be those having large out degrees in the trustee network, and they could enable an attacker to compromise many other users. Thus, we propose the T- Degree strategy to minimize the maximum out degree in the trustee network. Intuitively, T-Degree constrains that no users are selected as trustees by too many other users. Algorithm 2 shows our T-Degree strategy. T-Degree selects trustees for users one by one. For each user u that has adopted the trustee-based social authentication service, T-Degree selects his or her mufriends who’s current out degrees in the trustee network are the smallest as u's trustees; ties are broken uniform at random. V. EVALUATION RESULT The PLQP is a suit of protocols supporting privacy-preserving LBS in mobile application. It has high efficiency and achieves fine-grained control by exerting the CP-ABE scheme, which is similar to our work. Thus, in this section, we will compare our scheme with the PLQP scheme for evaluating the performance of our proposed scheme. The algorithms are implemented using the Big Integer library on a Windows 8.1 system with Intel CORE i7-4500U CPU@2.40 GHz and 8.00 G RAM. We have 10 tests in this experiment. Additionally, in each test, we use 1000 pairs of random locations for the publisher and the querier, respectively. We present the average results for each test in the following figures.It shows the detailed time cost for once location distance compute and location distance compare, respectively. It is obvious that the time cost at the publisher is always zero,
7.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1239 Fig : distance compute and location distance compare The comparison results of the total time cost for the aforementioned two operations, respectively. It can be seen that our scheme is much more efficient than PLQP. shows the comparison results of the detailed time cost for the aforementioned two operations at the publisher, querier and mobile cloud, respectively. To be more clear, we also present the comparison results in. From these figures, we get three points: At the querier, the time cost in our scheme is much less that that in PLQP. At the publisher, the time cost in our scheme is zero. At the mobile cloud, the time cost in PLQP is zero Fig : The comparison of each entity’s time cost between our scheme and PLQP Fig: The time comparison of performing location distance compute between our scheme and PLQP Fig: The time comparison of performing location distance compare between our scheme and PLQP In our system, the publisher can authorize the queries that he/she knows or not, such as his/her friends or someone who has similar interests. Hence, the queries may include attackers. If so, it is easy for the attacker to get a certain plaintext/ciphertext pair. Thus, our scheme has to be secure against the chosen plaintext attack. Next, we will prove it Conclusion In this Paper , here propose a user-service rating prediction approach by exploring users’ rating behaviors with considering four social network factors: user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users’ rating habits), and interpersonal rating behavior diffusion (related to users’ behavior diffusions). A concept of the rating schedule is proposed to represent user daily rating behavior. The similarity between user rating schedules is utilized to represent interpersonal rating behavior similarity. The factor of interpersonal rating behavior diffusion is proposed to deep understand users’ rating behaviors. We explore the user’s social circle, and split the social network into three components, direct friends, mutual friends, and the indirect friends, to deep understand social users’ rating behavior diffusions. These factors are fused together to improve the accuracy and applicability of predictions. We conduct a series of experiments in Yelp and Douban Movie datasets. The experimental results of our model show significant improvement. To further enhance the recommendation, check-in behaviors of users will be deeply explored by considering the factor of their multi-activity centers and the attribute of POIs. REFERENCES 1. K. Mao, L. Shou, J. Fan, G. Chen, and M. Kankanhalli, “Competencebased song recommendation: Matching songs to one’s singing skill,” IEEE Trans. Multimedia, vol. 17, no. 3, pp. 396–408, Mar. 2015.
8.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1240 2. R. Keshavan, A. Montanari, and S. Oh, “Matrix completion from noisy entries,” J. Mach. Learn. Res., vol. 11, pp. 2057–2078, 2010. 3. Q. Liu, E. Chen, H. Xiong, C. Ding, and J. Chen, “Enhancing collaborative filtering by user interest expansion via personalized ranking,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 1, pp. 218–233, Feb. 2012. 4. Y. Chen and J. Canny, “Recommending ephemeral items at web scale,” in Proc. SIGIR, 2011, pp. 1013–1022. 5. M. Harvey, M. J. Carman, I. Ruthven, and F. Crestani, “Bayesian latent variable models for collaborative item rating prediction,” in Proc. CIKM, 2011, pp. 699–708. 6. X.-W.Yang, H. Steck, andY. Liu, “Circle-based recommendation in online social networks,” in Proc. KDD, 2012, pp. 1267–1275. 7. Y. Zhou and L. Liu, “Social influence based clustering of heterogeneous information networks,” in Proc. KDD, 2013, pp. 338–346. 8. H. Gao, J. Tang, X. Hu, and H. Liu, “Exploring temporal effects for location recommendation on location-based social networks,” in Proc. RecSys, 2013, pp. 93–100. 9. X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 7, pp. 1763–1777, Jul. 2014. 10. K. Lee and K. Lee, “Using dynamically promoted experts for music recommendation,” IEEE Trans. Multimedia, vol. 16, no. 5, pp. 1201–1210, Aug. 2014. 11. Z. Wang et al., “Joint social and content recommendation for user generated videos in online social network,” IEEE Trans. Multimedia, vol. 15, no. 3, pp. 698–709, Apr. 2013.
Download now