TY - GEN
T1 - Probabilistic model for contextual retrieval
AU - Wen, Ji-Rong
AU - Lao, Ni
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2004
Y1 - 2004
N2 - Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.
AB - Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.
KW - Contextual Retrieval
KW - Probabilistic Model
KW - Query Expansion
KW - Query Log
UR - https://www.scopus.com/pages/publications/8644265239
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-8644265239&origin=recordpage
U2 - 10.1145/1008992.1009005
DO - 10.1145/1008992.1009005
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1581138814
SN - 9781581138818
T3 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 57
EP - 63
BT - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - Proceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Y2 - 25 July 2004 through 29 July 2004
ER -