计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 181-186.doi: 10.11896/j.issn.1002-137X.2017.08.032
印佳,程春玲,周剑
YIN Jia, CHENG Chun-ling and ZHOU Jian
摘要： 为了满足用户的多元化需求和提高用户查询的满意度,出现了多样化排序算法的研究,但是目前多样化排序算法在多样化和相关性之间不能达到很好的平衡,且查询处理效率不能完全适应实际的交互需求,为此提出了一种基于极小独立支配集的多样化排序算法。将多样化子集选取问题转化为无向加权图的极小独立支配集的求解问题,以此兼顾查询结果的多样化和相关性；在求解过程中通过引入抛弃子集的概念来减少冗余顶点对之间距离的比较,加快算法求解的速度。仿真实验表明,所提算法在多样化性能和查询处理效率方面有一定的提升。
| [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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