caa a22 4500 605470391 CHVBK 20210128100327.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1347-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1347-0 Learning to recommend with social contextual information from implicit feedback [Elektronische Daten] [Lei Guo, Jun Ma, Zhumin Chen, Huan Zhong] Recommender systems with social networks have been well studied in recent years. However, most of these methods ignore the social contextual information among users and items, which is significant and useful for predicting users' preferences in many recommendation problems. Moreover, most existing social recommendation methods have been proposed for the scenarios where users can provide explicit ratings. But in fact, the explicit feedback is not always available, most of the feedback in real social networks is not explicit but implicit. Motivated by above observations, we propose a unified ranking framework fusing social contextual information and common social relations for implicit feedback. Specifically, we first extend the user latent features by the implicit interest deduced from social context, and then we integrate the common social relations as factorization terms to further improve recommendation quality. Finally, we optimize our model in a Bayesian personalized ranking framework. The experiments on real-world dataset show that our approach outperforms the other state-of-the-art algorithms in terms of AUC, NDCG and Pre@3. This result demonstrates the importance of social context and common social relations for the formation of the implicit ratings. Springer-Verlag Berlin Heidelberg, 2014 Recommender system nationallicence Social context nationallicence Implicit feedback nationallicence Social recommendation nationallicence Bayesian personalized ranking nationallicence Guo Lei School of Computer Science and Technology, Shandong University, Jinan, China aut Ma Jun School of Computer Science and Technology, Shandong University, Jinan, China aut Chen Zhumin School of Computer Science and Technology, Shandong University, Jinan, China aut Zhong Huan School of Computer Science and Technology, Shandong University, Jinan, China aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1351-1362 1432-7643 19:5<1351 2015 19 500 https://doi.org/10.1007/s00500-014-1347-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1347-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Guo Lei School of Computer Science and Technology, Shandong University, Jinan, China aut NATIONALLICENCE 700 1- Ma Jun School of Computer Science and Technology, Shandong University, Jinan, China aut NATIONALLICENCE 700 1- Chen Zhumin School of Computer Science and Technology, Shandong University, Jinan, China aut NATIONALLICENCE 700 1- Zhong Huan School of Computer Science and Technology, Shandong University, Jinan, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1351-1362 1432-7643 19:5<1351 2015 19 500