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Top-k learning to rank: labeling, ranking and evaluation
所属机构名称:中国科学院计算技术研究所
会议名称:The 35th international ACM SIGIR conference on Research and development in information retrieval
时间:2012.8.8
成果类型:会议
相关项目:基于大规模用户数据的推荐技术研究
作者:
Niu, Shuzi|Guo, Jiafeng|Lan, Yanyan|Cheng, Xueqi|
同会议论文项目
基于大规模用户数据的推荐技术研究
期刊论文 14
会议论文 24
专利 2
同项目会议论文
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Clustering Short Text Using Ncut-weighted Non-negative Matrix Factorization
A New Probabilistic Model for Top-k Ranking Problem
Recommending high utility query via session-flow graph
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Learning topics in short texts by non-negative matrix factorization on term correlation matrix
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Bipartite Graph Based Entity Ranking for Related Entity Finding
Group sparse topical coding : from code to topic
A Novel Relational Learning-to-Rank Approach for Topic-Focused Multi-Document Summarization
More Than Relevance: High Utility Query Recommendation By Mining Users' Search Behaviors
Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space
Supervised Lazy Random Walk for Topic-Focused Multi-document Summarization
Multi-select faceted navigation based on minimum description length principle
Context-aware query recommendation by learning high-order relation in query logs
A unified framework for recommending diverse and relevant queries
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