Temporal Sensitive Learning to Rank Method for Microblog Search
WANG Shuxin1, WEI Bingjie2, LU Xiao2, WANG Bin3
1. University of Chinese Academy of Sciences, Institute of Computing Technology, CAS, Beijing 100190, China； 2. National Computer Network Emergency Response Technical Team/Coordination Center, Beijing 100029, China; 3. Institute of Information Engineering, CAS, Beijing 100093, China
Abstract：Microblog search has become a hot research problem in information retrieval area in recent years. Related work shows that most queries in microblog search are time-sensitive. To address this problem, many existing methods were proposed based on different time-sensitive assumptions, such as, “the newer of a document, the more important it is” or “the closer to the peak point a document is, the more important it is”. All these methods have improved retrieval effectiveness somehow. However, it is hard to summarize the temporal role in ranking of microblog search to one straight forward assumption as above. In this paper, our study on temporal distributions of relevant documents of different queries shows the complexity of temporal role in ranking; therefore, simple straight forward assumptions are not accurate. We proposed to use the temporal and entity evidences of query-document pairs to train a time-sensitive learning to rank model to tackle this problem. As for temporal features, both global features of query and local features of query-documents pair are extracted. Experimental results show that TLTR significantly improves the retrieval effectiveness over existing time aware ranking models on TREC Microblog Track 2011—2012 data set.
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