TY - GEN
T1 - Long tail recommender utilizing information diffusion theory
AU - Ishikawa, Masayuki
AU - Geczy, Peter
AU - Izumi, Noriaki
AU - Yamaguchi, Takahira
PY - 2008
Y1 - 2008
N2 - Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.
AB - Our approach aims to provide a mechanism for recommending long tail items to knowledge workers. The approach employs collaborative filtering using browsing features of identified key population of the diffusion of information. We conducted analytic experiment for a novel recommendation algorithm based on the browsing features of identified selected users and discovered that the first 10 users accessing a particular page play the key role in information spread. The evaluation indicated that our approach is effective for long tail recommendation.
UR - http://www.scopus.com/inward/record.url?scp=62949199025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62949199025&partnerID=8YFLogxK
U2 - 10.1109/WIIAT.2008.352
DO - 10.1109/WIIAT.2008.352
M3 - Conference contribution
AN - SCOPUS:62949199025
SN - 9780769534961
T3 - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
SP - 785
EP - 788
BT - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
T2 - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Y2 - 9 December 2008 through 12 December 2008
ER -