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
Making recommendations by integrating information from multiple social networks
Date
2016-7-1
Author
Ozsoy, Makbule Gulcin
Polat, Faruk
Alhajj, Reda
Metadata
Show full item record
Item Usage Stats
413
views
0
downloads
Cite This
It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted experiments have shown that using multiple features from multiple sources is expected to produce a more concrete and wider perspective of user's behavior and preferences. This leads to improved recommendation outcome.
Subject Keywords
Recommendation systems
,
Individual modeling
,
Multiple data sources
,
Social networking platforms
,
Multiple perspective based analysis
,
User behavior
URI
https://hdl.handle.net/11511/28164
Journal
Applied Intelligence
DOI
https://doi.org/10.1007/s10489-016-0803-1
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Modeling Individuals and Making Recommendations Using Multiple Social Networks
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2015-08-28)
Web-based platforms, such as social networks, review web-sites, and e-commerce web-sites, commonly use recommendation systems to serve their users. The common practice is to have each platform captures and maintains data related to its own users. Later the data is analyzed to produce user specific recommendations. We argue that recommendations could be enriched by considering data consolidated from multiple sources instead of limiting the analysis to data captured from a single source. Integrating data from...
A Content boosted hybrid recommendatıon system
Çapraz, Seval; Temizer, Selim; Department of Computer Engineering (2016)
Nowadays, most of e-commerce and social media sites use recommendation systems to help users find more relevant products easily. The key feature of recommendation is personalization which means different products are being offered for different users according to each user s interests. In literature, there are a lot of algorithms and tools which implement recommendation systems. The most common techniques for recommendation systems include Collaborative Filtering (CF) and Content-Based Filtering (CBF). To incre...
Determining online consumer typologies and their shopping behaviors in B2C e-commerce platforms
Huseynov, Farid; Demirörs, Onur; Türetken, Oktay; Department of Information Systems (2016)
Business-to-consumer (B2C) e-commerce is the service or product exchange from businesses to consumers over the Internet. B2C e-commerce enables customers to easily compare offered products and services, to find cheaper and better ones from many alternatives and to shop from any given store without physically visiting them. Despite those conveniences provided by B2C e-commerce, a large number of customers prefer to stay away from the idea of shopping over internet due to several factors. For better customer ...
IDENTIFICATION OF THE FACTORS AFFECTING CUSTOMER ENGAGEMENT IN ONLINE BRAND COMMUNITIES: A PILOT STUDY
Özkan Yıldırım, Sevgi (2019-12-01)
Social commerce is defined as a new wave of e-commerce in which traditional e-commerce is mediatedby social media and social networking services in order to promote online transactions andshopping-related information exchanges. One of the main application of social commerce is ‘online brandcommunities’. Although utilization of social media in order to advance customer brand engagement hasproliferated significantly in recent years, most of the companies have not met the expected level ofengagement of their c...
Using ontology based web usage mining and object clustering for recommendation
Yılmaz, Hakan; Karagöz, Pınar; Department of Computer Engineering (2010)
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. Keyword-based approaches, collaborative and content filtering techniques have been tried and used over the years each having their own shortcomings. While keyword based approaches are naive and do not take content or context into account collaborative and content filtering tech...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. G. Ozsoy, F. Polat, and R. Alhajj, “Making recommendations by integrating information from multiple social networks,”
Applied Intelligence
, pp. 1047–1065, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28164.