Browsing by Author "Kayaalp, Mehmet"

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Citation Count: Ozyer, Sibel T. untranslated, Kayaalp, M., "A Collaborative and Content Based Event Recommendation System Integrated With Data Collection Scrapers and Services At A Social Networking Site", Proceedings of the 2009 International Conference On Advances in Social Network Analysis and Mining, Asonam 2009, pp. 113-118, (2009).
A Collaborative and Content Based Event Recommendation System Integrated With Data Collection Scrapers and Services At A Social Networking Site
(IEEE Computer Society, 2009) Kayaalp, Mehmet; Özyer, T.; Özyer, Sibel T.; 18980
There are many activities that people prefer/opt out attending and these events are announced for attracting people. An intelligent recommendation system can be used in a social networking site in order to recommend people according to content and collaboration assessment. This study is an effort to recommend events to users within a social networking site. It can be any networking environment. We have used social environment that has been designed as a facebook1 application. Our application has also been integrated with several web sites. System collects event data from several related web sites either by using web services or web scraping. It also permits users rating events they have attended or planned. Given the social network between people, system tries to recommend upcoming events to users. For this purpose a combination of content based and collaborative filtering has been used. We have also taken geographical location info and social concept of an event. © 2009 IEEE.
Citation Count: Ozyer, Sibel T.; Ozyer, T.; Kayaalp, M., "A Mash-Up Application Utilizing Hybridized Filtering Techniques for Recommending Events At A Social Networking Site", Social Network Analysis and Mining, Vol. 1, No. 3, (2011).
A Mash-Up Application Utilizing Hybridized Filtering Techniques for Recommending Events At A Social Networking Site
(Springer-Verlag Wien, 2011) Kayaalp, Mehmet; Özyer, Tansel; T. Özyer, Sibel; 18980
Event recommendation is one way of gathering people having same likes/dislikes. In today’s world, many mass amounts of events are organized at different locations and times. Generally, cliques of people are fans of some specific events. They attend together based on each other’s recommendation. Generally, there are many activities that people prefer/opt out attending and these events are announced for attracting relevant people. Rather than, peer-to-peer oracles of a local group of people, or sentiments of people from different sources, an intelligent recommendation system can be used at a social networking site in order to recommend people in collaborative and content basis within a social networking site. We have used an existing social environment (http://www.facebook.com) for deployment. Our application has also been integrated with several web sites for collecting information for assessment. Our system has been designed in modules so that it is open to new data sources either by using web services or web scraping. Currently, our application is yet an application that permits users rate events; they have attended or have beliefs on them. Given the social network between people, system tries to recommend upcoming events to users. For this purpose, we have exploited the fact that a similarity relationship between different events can exist in terms of both content and collaborative filtering. Geographical locations have an impact so; we have also taken geographical location information and social concept of an event. Eventually, our system integrates different sources in facebook (http://www.facebook.com) for doing recommendation between people in close relationship. We have performed experiments among a group of students. Experiments led us have promising results.