TY - GEN
T1 - IDianNao
T2 - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
AU - Fang, Wen Chieh
AU - Yang, Pei Ching
AU - Hsieh, Meng Che
AU - Chiang, Jung Hsien
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Older volunteers can provide valuable, needed services for communities and organizations. However, older people who wish to help often have difficulty finding the right volunteer opportunities. In this paper, we present an online system that exploits recommender system techniques to provide volunteer opportunities for older people. We rank the opportunities according to a weight sum of four scores, which are neighbor score, click score, time score, and area score. For each opportunity, we first select the neighborhood of the user by considering preference similarity for the opportunity and the organization as well as the social interactions involved. We then compute the neighbor score as the ratio of neighbors choosing the target opportunity to all neighbors. The click score is calculated as the rate that an user clicks on the interested opportunity pages. The time and area scores indicate the availability of time and the ability to travel, respectively. For the particular volunteer opportunity recommendations, we further consider the coverage issue by adding a deadline factor when computing the opportunity score. At the end of the paper, we report the system implementation and provide a summary.
AB - Older volunteers can provide valuable, needed services for communities and organizations. However, older people who wish to help often have difficulty finding the right volunteer opportunities. In this paper, we present an online system that exploits recommender system techniques to provide volunteer opportunities for older people. We rank the opportunities according to a weight sum of four scores, which are neighbor score, click score, time score, and area score. For each opportunity, we first select the neighborhood of the user by considering preference similarity for the opportunity and the organization as well as the social interactions involved. We then compute the neighbor score as the ratio of neighbors choosing the target opportunity to all neighbors. The click score is calculated as the rate that an user clicks on the interested opportunity pages. The time and area scores indicate the availability of time and the ability to travel, respectively. For the particular volunteer opportunity recommendations, we further consider the coverage issue by adding a deadline factor when computing the opportunity score. At the end of the paper, we report the system implementation and provide a summary.
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U2 - 10.1007/978-3-319-19066-2_66
DO - 10.1007/978-3-319-19066-2_66
M3 - Conference contribution
AN - SCOPUS:84944691169
SN - 9783319190655
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 683
EP - 691
BT - Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Proceedings
A2 - Lee, Chang-Hwan
A2 - Kim, Yongdai
A2 - Kwon, Young Sig
A2 - Kim, Juntae
A2 - Ali, Moonis
PB - Springer Verlag
Y2 - 10 June 2015 through 12 June 2015
ER -