Computer Science ›› 2017, Vol. 44 ›› Issue (8): 181-186.doi: 10.11896/j.issn.1002-137X.2017.08.032
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YIN Jia, CHENG Chun-ling and ZHOU Jian
| [1] SANTOS R L T,PENG J,MACDONALD C,et al.ExplicitSearch Result Diversification through Sub-queries[C]∥Proceedings of the 32nd European Conference on Advances in Information Retrieval.ACM,2010:87-99. [2] OZDEMIRAY A M,ALTINGOVDE I S.Explicit search result diversification using score and rank aggregation methods[J].Journal of the Association for Information Science & Technology,2015,66(2):1212-1228. [3] REN P J,CHEN Z M,MA J,et al.Search result diversification combing semantic and temporal intent[J].Chinese Journal of Computers,2015,8(10):2076-2091.(in Chinese) 任鹏杰,陈竹敏,马军,等.一种综合语义和时效性意图的检索结果多样化方法[J].计算机学报,2015,38(10):2076-2091. [4] WU S L,HUANG C L.Search result diversification via data fusion[C]∥Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrie-val.ACM,2014:827-830. [5] YU H T,REN F.Search Result Diversification via Filling Up Multiple Knapsacks[C]∥Proceedings of the 23rd ACM International Conference on Information and Knowledge Management.ACM,2014:609-618. [6] HU S,DOU Z,WANG X,et al.Search Result Diversification Based on Hierarchical Intents[C]∥Proceedings of the 24th ACM International on Conference on Information and Know-ledge Management.ACM,2015:63-72. [7] RADLINSKI F,KLEINBERG R,JOACHIMS T.Learning diverse rankings with multi-armed bandits[C]∥Proceedings of the 25th International Conference on Machine Learning.ACM,2008:784-791. [8] SCHUTH A,OOSTERHUIUS H,WHITESON S,et al.Multileave Gradient Descent for Fast Online Learning to Rank[C]∥Proceedings of the Ninth ACM International Conference on Web Search and Data Mining.ACM,2016:457-466. [9] OOSTERHUIUS H,SCHUTH A,DE RIJKE M.Probabilistic Multileave Gradient Descent[C]∥Proceedings of the 38th European Conference on IR Research.2016:661-668. [10] CARBONELL J,GOLDSTEIN J.The use of MMR,diversity-based re-ranking for reordering documents and producing summaries[C]∥Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,1998:335-336. [11] XIA L,XU J,LAN Y,et al.Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Mea-sures[C]∥Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.2015:113-122. [12] ZHU X J,GOLDBERG,et al.Improving Diversity in Rankingusing Absorbing Random Walks[C]∥Proceedings of the North American Chapter of the Association for Computational Linguistics Conference on Human Language Technology.2007:97-104. [13] ZHU Y,LAN Y,GUO J,et al.Learning for search result diversification[C]∥Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval.ACM,2014:293-302. [14] CARPINETO.AMBIENT Dataset[EB/OL].http://credo.fub.it/ambient. [15] ZHAI C X,COHEN W W,LAFFERTY J.Beyond independent relevance:methods and evaluation metrics for subtopic retrieval[C]∥Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2003:10-17. |
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