By Shaoping Ma, Ji-Rong Wen, Yiqun Liu, Zhicheng Dou, Min Zhang, Yi Chang, Xin Zhao
This ebook constitutes the refereed court cases of the twelfth info Retrieval Societies convention, AIRS 2016, held in Beijing, China, in November/December 2016.
The 21 complete papers awarded including eleven brief papers have been conscientiously reviewed and chosen from fifty seven submissions. the ultimate programme of AIRS 2015 is split within the following tracks: IR types and theories; desktop studying and information mining for IR; IR purposes and person modeling; personalization and advice; and IR evaluation.
Read Online or Download Information Retrieval Technology: 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 – December 2, 2016, Proceedings PDF
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Additional info for Information Retrieval Technology: 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 – December 2, 2016, Proceedings
In search, for the query of “best game in the history”, documents about “most popular game”, “hardest game” and “strongest game” are promoted to higher positions, which enhances the relevance as well as the richness of search result. However, there are also some bad cases, mainly due to noises in the synonym dictionary. For example, in one experiment our mining algorithm identiﬁes “google” and “baidu” as synonyms. Then for the query of “google map”, a document about “baidu map” is ranked higher than a document about “google earth”.
Consequently, all rows of Lx will converge to the eigenvector (with the largest eigenvalue) of the matrix (λ2 ρ2 )−1 CC T , and so will be all rows of Ly . Thus, the optimal parameters L∗x and L∗y are both matrices of rank one. This justiﬁes the necessity of regularization on the matching matrix LTx Ly . 2 Incorporating Semantic Knowledge to Latent Matching Model A latent matching model trained with the method described in the previous section can perform well for head queries and documents, since it can capture the matching information from click-through data.
Other unnormalised measures such as Time-Biased Gain , U-measure  and those designed for diversiﬁed search may be handled similarly. Furthermore, we have demonstrated that discrete measures such as nG@1, which clearly violate the normality assumptions, can be “smoothed” to some extent by applying std-AB. Recall that topic set size design assumes The Eﬀect of Score Standardisation on Topic Set Size Design 27 that the scores are indepent and identically distributed: that the scores for system i obey N (μi , σ 2 ).