Poster Papers

A Comparison of How Demographic Data Affects Recommendation

AuthorAlan Said, Till Plumbaum, Ernesto William De Luca, Sahin Albayrak
SourceUMAP 2011, Poster and Demo Session 

Recommender systems attempt to find relevant data for their users. As the amount of data available in the Web becomes larger, this task becomes increasingly harder. In this paper we present a comparison of recommendation results when using different demographic features (age, location, gender, etc.) commonly available in online movie recommendation communities. We assume that demographic information holds implicit information about users' taste and interests, and present results of a simple method that extends standard collaborative filtering algorithms to include one or several of these features. We evaluate the our assumption in movie recommendation scenario and combine different features in order to improve recommendation results.