Title :
Incorporating Tie Strength in Robust Social Recommendation
Author :
Youliang Zhong ; Jian Yang ; Nugroho, Robertus
Author_Institution :
Dept. of Comput., Macquarie Univ., Sydney, NSW, Australia
Abstract :
In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-the-art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.
Keywords :
least mean squares methods; recommender systems; social sciences computing; data outliers; factorization scheme; integrated social relationship measurement; least absolute errors; precision measures; recall measures; robust social recommendation; root mean squared error; social media services; tie strength; Linear programming; Mathematical model; Measurement; Media; Recommender systems; Robustness; Social network services; Recommender Systems; Robust Matrix Factorization; Social Media; Social Recommendation; Tie Strength;
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
DOI :
10.1109/BigDataCongress.2015.19