Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Time Preference aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information
Date
2016-08-21
Author
Ozsoy, Makbule Gulcin
Polat, Faruk
Alhajj, Reda
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
260
views
0
downloads
Cite This
Social networks and location based social networks have many active users who provide various kind of data, such as where they have been, who their friends are, which items they like more, when they go to a venue. Location, social network and temporal information provided by them can be used by recommendation systems to give more accurate suggestions. Also, recommendation systems can provide dynamic recommendations based on the users' preferences, such that they can give different recommendations for different hours of the day or different days of the week. In this paper, we propose a recommendation system which considers the users' temporal preference to give dynamic recommendation. The recommendation method uses multi-objective optimization approach and gives point of interest (POI) recommendation using several different criteria, namely past check-in locations, hometown of users, time of check-ins, friendship and influence among users.
Subject Keywords
Collaboration
,
Context
,
Optimization
,
Correlation
,
Electronic mail
,
Facebook
URI
https://hdl.handle.net/11511/54550
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Context aware friend recommendation for location based social networks using random walk
Bağcı, Hakan; Karagöz, Pınar (null; 2016-04-10)
The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN...
Context-aware location recommendation by using a random walk-based approach
Bagci, Hakan; Karagöz, Pınar (2016-05-01)
The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized ...
Multi-Objective Optimization Based Location and Social Network Aware Recommendation
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2014-10-25)
Social networks, personal blog pages, on-line transaction web-sites, expertise web pages and location based social networks provide an attractive platform for millions of users to share opinions, comments, ratings, etc. Having this kind of diverse and comprehensive information leads to difficulties for users to reach the most appropriate and reliable conclusions. Recommendation systems form one of the solutions to deal with the information overload problem by providing personalized services. Using spatial, ...
TRUST-AWARE LOCATION RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Cantürk, Deniz; Karagöz, Pınar; Department of Computer Engineering (2021-8-9)
Users can share their location with other social network users through location-embedded information in LBSNs (Location-Based Social Network). LBSNs contain useful resources, such as user check-in activities, for building a personalized recommender system. Trust in social networks is another important concept that has been integrated into a recommendation system in various settings. In this thesis, we propose two novel techniques for location recommendation, TLoRW and SgWalk, to improve recommendation perfo...
Contextual Feature Analysis to Improve Link Prediction for Location Based Social Networks
Bayrak, Ahmet Engin; Polat, Faruk (2014-10-01)
In recent years, people started to communicate, interact, maintain relationship and share data (image, video, note, location, etc.) with their acquaintances through varying online social network sites. Online social networks with location and time sharing/interaction among people are called Location Based Social Networks (LBSNs). Link prediction in social networks aims at predicting future possible links for representing the real life relations better. In this work, we studied the link prediction problem an...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. G. Ozsoy, F. Polat, and R. Alhajj, “Time Preference aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54550.