TY - GEN
T1 - Social activity recognition and recommendation based on mobile sound sensing
AU - Yang, Yao
AU - Guo, Bin
AU - Yu, Zhiwen
AU - He, Huilei
PY - 2013
Y1 - 2013
N2 - Nowadays, as social activities in the real world are getting more and more popular, it is important to understand the social interaction features and the social contexts of users (meetings, social gatherings, etc.) to support social activity organization. By leveraging the social context and activities of users, various services can be provided (e.g., recommendation of friends, activities, etc.). In this paper, we present a mobile phone based social activity recommendation system based on background sound recognition. We first propose a method to analyze background sound signals of different activities, where a combination of the Mel frequency cepstral coefficients (MFCCs) and several other sound features are extracted. Based on these features, the Dynamic Time Warping (DTW) with limited-path searching algorithm is used to recognize different human activities. The information online (friend relationship and online interaction history) is adopted to measure the intimacy among users. Finally, we present an algorithm that combines the distance, user preference and intimacy features for activity ranking and recommendation. Experimental results show that the proposed approach is effective and useful.
AB - Nowadays, as social activities in the real world are getting more and more popular, it is important to understand the social interaction features and the social contexts of users (meetings, social gatherings, etc.) to support social activity organization. By leveraging the social context and activities of users, various services can be provided (e.g., recommendation of friends, activities, etc.). In this paper, we present a mobile phone based social activity recommendation system based on background sound recognition. We first propose a method to analyze background sound signals of different activities, where a combination of the Mel frequency cepstral coefficients (MFCCs) and several other sound features are extracted. Based on these features, the Dynamic Time Warping (DTW) with limited-path searching algorithm is used to recognize different human activities. The information online (friend relationship and online interaction history) is adopted to measure the intimacy among users. Finally, we present an algorithm that combines the distance, user preference and intimacy features for activity ranking and recommendation. Experimental results show that the proposed approach is effective and useful.
KW - Activity recommendation
KW - Background sound recognition
KW - DTW algorithm
KW - Friend intimacy
KW - Mel frequency cepstral coefficients (MFCC)
KW - Social context awareness
UR - http://www.scopus.com/inward/record.url?scp=84894184239&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC.2013.61
DO - 10.1109/UIC-ATC.2013.61
M3 - 会议稿件
AN - SCOPUS:84894184239
SN - 9781479924813
T3 - Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013
SP - 103
EP - 110
BT - Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013
T2 - 10th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and 10th IEEE International Conference on Autonomic and Trusted Computing, ATC 2013
Y2 - 18 December 2013 through 21 December 2013
ER -