TY - GEN
T1 - Re-ranking voting-based answers by discarding user behavior biases
AU - Wei, Xiaochi
AU - Huang, Heyan
AU - Lin, Chin Yew
AU - Xin, Xin
AU - Mao, Xianling
AU - Wang, Shangguang
PY - 2015
Y1 - 2015
N2 - The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user's attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15.1% in precision@1, and 11.7% in the mean reciprocal rank. A case st-udy on a manually labeled dataset futher supports the effectiveness of the proposed model.
AB - The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user's attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15.1% in precision@1, and 11.7% in the mean reciprocal rank. A case st-udy on a manually labeled dataset futher supports the effectiveness of the proposed model.
UR - https://www.scopus.com/pages/publications/84949793204
M3 - Conference contribution
AN - SCOPUS:84949793204
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2380
EP - 2386
BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
A2 - Wooldridge, Michael
A2 - Yang, Qiang
PB - International Joint Conferences on Artificial Intelligence
T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Y2 - 25 July 2015 through 31 July 2015
ER -