[1] Dakka W, Gravano L, Ipeirotis PG.Answering general time-sensitive queries[J].Knowledge and Data Engineering, IEEE Transactions on, 2012;24(2):220-235.
[2] Fu C-L, Silver D.Time-sensitive Sampling for Spam Filtering[M].Ontario, Canda:Springer, 2004.551-553.
[3] Shokouhi M, Radinsky K.Time-sensitive query auto-completion[A].Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Portland:ACM, 2012.601-610.
[4] Zhang R, Chang Y, Zheng Z, Metzler D, Nie J-y.Search result re-ranking by feedback control adjustment for time-sensitive query[A].Proceedings of Human Language Technologies:The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume[C].Boulder:Association for Computational Linguistics, 2009.165-168.
[5] Efron M.Linear time series models for term weighting in information retrieval[J].Journal of the American Society for Information Science and Technology, 2010, 61(7):1299-1312.
[6] Dong A, Zhang R, Kolari P, Bai J, Diaz F, Chang Y, et al.Time is of the essence:Improving recency ranking using twitter data[A].Proceedings of the 19th International Conference on World Wide Web[C].Raleigh:ACM, 2010.331-340.
[7] Radinsky K, Svore K, Dumais S, Teevan J, Bocharov A, Horvitz E.Modeling and predicting behavioral dynamics on the web[A].Proceedings of the 21st International Conference on World Wide Web[C].Portland:ACM, 2012.599-608.
[8] Bar-Yossef Z, Kraus N.Context-sensitive query auto-completion[A].Proceedings of the 20th International Conference on World Wide Web[C].Hyderabad:ACM, 2011.107-116.
[9] Bast H, Weber I.Type less, find more:fast autocompletion search with a succinct index[A].Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Seattle:ACM, 2006.364-371.
[10] Chaudhuri S, Kaushik R.Extending autocompletion to tolerate errors[A].Proceedings of the 35th SIGMOD International Conference on Management of Data[C].Providence:ACM, 2009.707-718.
[11] Ji S, Li G, Li C, Feng J.Efficient interactive fuzzy keyword search[A].Proceedings of the 18th International Conference on World Wide Web[C].New York:ACM, 2009.371-380.
[12] Li G, Wang J, Li C, Feng J.Supporting efficient top-k queries in type-ahead search[A].Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Portland:ACM, 2012.355-364.
[13] Iosif E, Potamianos A.Unsupervised semantic similarity computation between terms using web documents[J].Knowledge and Data Engineering, IEEE Transactions on, 2010, 22(11):1637-1647.
[14] Hwang H, Lauw HW, Getoor L, Ntoulas A.Organizing user search histories[J].Knowledge and Data Engineering, IEEE Transactions on, 2012, 24(5):912-925.
[15] Khribi MK, Jemni M, Nasraoui O.Automatic recommendations forelearning personalization based on web usage mining techniques and information retrieval[A].Advanced Learning Technologies, 2008 ICALT'08 Eighth IEEE International Conference on[C].Piscataway:IEEE, 2008.241-245.
[16] Ono C, Kurokawa M, Motomura Y, Asoh H.A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation Andpromotion[M].Berlin Heidelberg:Springer, 2007.247-257.
[17] Xu H-L, Wu X, Li X, Yan B.Comparison study of Internet recommendation system[J].Journal of Software, 2009, 20(2):350-362.
[18] Yu Z, Zhou X, Zhang D, Chin C-Y, Wang X.Supporting context-aware media recommendations for smart phones[J].Pervasive Computing, IEEE, 2006, 5(3):68-75.
[19] 付博, 赵世奇, 刘挺.Web查询日志研究综述[J].电子学报, 2013, 41(9):1800-1808. Fu Bo, Zhao Shiqi, Liu Ting.Research on analysis and mining of web query logs[J].Acta Electronica Sinica, 2013, 41(9):1800-1808.
[20] Alfonseca E, Ciaramita M, Hall K.Gazpacho and summer rash:Lexical relationships from temporal patterns of Web search queries[A].Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing[C].Philadelphia:Association for Computational Linguistics, 2009.1046-1055.
[21] Metzler D, Jones R, Peng F, Zhang R.Improving search relevance for implicitly temporal queries[A].Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Boston:ACM, 2009.700-701.
[22] Berry MW, Browne M.Lecture Notes in Data Mining[M].Singapore:World Scientific, 2006.27-38
[23] Jones R, Diaz F.Temporal profiles of queries[J].ACM Transactions on Information Systems (TOIS), 2007, 25(3):14.
[24] Kim HD, Nikitin D, Zhai C, Castellanos M, Hsu M.Informationretrieval with time series query[A].Proceedings of the 2013 Conference on the Theory of Information Retrieval[C].New York:ACM, 2013.14.
[25] Shokouhi M.Detecting seasonal queries bytime-series analysis[A].Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Beijing:ACM, 2011.1171-1712.
[26] Hamilton JD.TimeSeries Analysis[M].Cambridge:Cambridge University Press.1994.
[27] Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A.Incorporating contextual information in recommender systems using a multidimensional approach[J].ACM Transactions on Information Systems (TOIS), 2005, 23(1):103-145.
[28] Adomavicius G, Tuzhilin A.Context-Aware Recommender Systems[M].New York:Springer, 2011.217-253.
[29] Diaz F.Integration of news content into web results[A].Proceedings of the Second ACM International Conference on Web Search and Data Mining[C].Barcelona:ACM, 2009.182-191.
[30] König AC, Gamon M, Wu Q.Click-through prediction for news queries[A].Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information retrieval[C].Boston:ACM, 2009.347-354.
[31] Dong A, Chang Y, Zheng Z, Mishne G, Bai J, Zhang R, et al.Towards recency ranking in web search[A].Proceedings of the Third ACM International Conference on Web Search and Data Mining[C].New York:ACM, 2010.11-20.
[32] Kanhabua N, Nørvåg K.Determining Time of Queries for Re-Ranking Search Results[M].Berlin Heidelberg:Research and Advanced Technology for Digital Libraries, 2010.261-72.
[33] Agichtein E, Brill E, Dumais S.Improving web search ranking by incorporating user behavior information[A].Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Seattle:ACM, 2006.19-26.
[34] Murata M, Toda H, Matsuura Y, Kataoka R, Mochizuki T.Detecting periodic changes in search intentions in a search engine[A].Proceedings of the 19th ACM International Conference on Information and Knowledge Management[C].Toronto:ACM, 2010.1525-1528.
[35] Anand A, Bedathur S, Berberich K, Schenkel R.Index maintenance for time-travel text search[A].Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Portland:ACM, 2012.235-244.
[36] Vlachos M, Meek C, Vagena Z, Gunopulos D.Identifying similarities, periodicities and bursts for online search queries[A].Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data[C].New York:ACM, 2004.131-142.
[37] Keogh EJ, Pazzani MJ.Relevance feedback retrieval of time series data[A].Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Berkeley:ACM, 1999.183-190.
[38] Wang TD, Deshpande A, Shneiderman B.A temporal pattern search algorithm for personal history event visualization[J].Knowledge and Data Engineering, IEEE Transactions on, 2012, 24(5):799-812.
[39] Zhang Z-K, Liu C, Zhang Y-C, Zhou T.Solving the cold-start problem in recommender systems with social tags[J].EPL (Europhysics Letters), 2010, 92(2):28002.
[40] Gantner Z, Drumond L, Freudenthaler C, Rendle S, Schmidt-Thieme L.Learning attribute-to-feature mappings for cold-start recommendations[A].Data Mining (ICDM), 2010 IEEE 10th International Conference on[C].Shenzhen:IEEE, 2010.176-185.
[41] Blerina Lika, Kostas Kolomvatsos, Stathes Hadjiefthymiades.Facing the cold start problem in recommender systems[J].Expert Syst, 2014, 41(4):2065-2073.
[42] JLin, KSugiyama, M-Y Kan, T-S Chua.Addressing cold-start in app recommendation:Latent user models constructed from twitter followers[A].36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013) [C].Dublin:ACM, 2013.283-292.
[43] Heping Li, Feng Zhang, Shuwu Zhang.Multi-feature hierarchical topic models for human behavior recognition[J].Science China Information Sciences, 2014, 57(9):1-15.
[44] Mi Zhang, Jie Tang, Xuchen Zhang, Xiangyang Xue.Addressing cold start in recommender systems:a semi-supervised co-training algorithm[A]. 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'14) [C].Gold Coast:ACM, 2014.73-82.
[45] Ke Zhou, Shuang-Hong Yang, Hongyuan Zha.Functional matrix factorizations for cold-start recommendation[A].34th International ACM SIGIR Conference on Research and Development in Information Retrieval[C].New York:ACM, 2011.315-324. |