Abstract
In this paper, we aim to deal with the deficiency of current information retrieval models by integrating the concept of relevance into the generation model from different topical aspects of the query. We study a series of relevance-dependent topic models. These models are adapted from the latent Dirichlet allocation model. They are distinguished by how the notation of query-document relevance, which is critical in information retrieval, is introduced in the modeling framework. Approximate yet efficient parameter estimation methods based on the Gibbs sampling technique are employed for parameter estimation. The results of experiments evaluated on the Text REtrieval Conference Corpus in terms of the mean average precision (mAP) demonstrate the superiority of the proposed models.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Andrzejewski, D., Buttler, D.: Latent Topic Feedback for Information Retrieval. In: Proceedings of ACM KDD Conference on Knowledge Discovery and Data Mining, pp. 600–608 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3(4-5), 993–1022 (2003)
Chemudugunta, C., Smyth, P., Steyvers, M.: Modeling general and specific aspects of documents with a probabilistic topic model. Advances in Neural Information Processing Systems, 241–248 (2007)
Chien, J.T., Wu, M.S.: Adaptive Bayesian latent semantic analysis. IEEE Transactions on Audio, Speech, and Language Processing 16(1), 198–207 (2008)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences, 5228–5235 (2004)
Heidel, A., Chang, H.A., Lee, L.S.: Language Model Adaptation Using Latent Dirichlet Allocation and an Efficient Topic Inference Algorithm. In: Proceedings of INTERSPEECH, pp. 2361–2364 (2007)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 50–57 (1999)
Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 329–338 (1993)
Levy, M., Sandler, M.: Learning latent semantic models for music from social tags. Journal of New Music Research 2(37), 137–150 (2008)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic Modeling with Network Regularization. In: Proceeding of the 17th International Conference on World Wide Web, pp. 101–110 (2008)
Minka, T., Lafferty, J.D.: Expectation-propagation for the generative aspect model. In: Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, pp. 352–359 (2002)
Scholer, F., Williams, H.E.: Query association for effective retrieval. In: Proceedings of the ACM CIKM International Conference on Information and Knowledge Management, pp. 324–331 (2002)
Song, F., Croft, W.B.: A general language model for information retrieval. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 279–280 (1999)
Song, W., Yu, Z., Liu, T., Li, S.: Bridging topic modeling and personalized search. In: Proceedings of COLING, pp. 1167–1175 (2010)
Tao, T., Wang, X., Mei, Q., Zhai, C.: Language Model Information Retrieval with Document Expansion. In: Proceedings of HLT/NAACL, pp. 407–414 (2006)
Wallach, H.: Topic Modeling: Beyond Bag-of-Words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977–984 (2006)
Wang, J.C., Wu, M.S., Wang, H.M., Jeng, S.K.: Query by Multi-tags with Multi-level Preferences for Content-based Music Retrieval. In: IEEE International Conference on Multimedia and Expo (ICME) (2011)
Wang, X., McCallum, A., Wei, X.: Topical N-Grams: phrase and topic discovery, with an application to information retrieval. In: Seventh IEEE International Conference on Data Mining (ICDM), pp. 697–702 (2007)
Wei, X., Croft, W.B.: LDA-based document models for ad-hoc retrieval. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 178–185 (2006)
Wu, M.S., Lee, H.S., Wang, H.M.: Exploiting semantic associative information in topic modeling. In: Proceedings of the IEEE Workshop on Spoken Language Technology, pp. 384–388 (2010)
Yi, X., Allan, J.: A Comparative Study of Utilizing Topic Models for Information Retrieval. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 29–41. Springer, Heidelberg (2009)
Zhai, C.: Statistical Language Models for Information Retrieval: A Critical Review. Foundations and Trends in Information Retrieval 3(2), 137–213 (2008)
Zhai, C., Lafferty, J.D.: A study of smoothing methods for language models applied to Ad Hoc information retrieval. In: Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 334–342 (2001)
Zhai, C., Lafferty, J.D.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the CIKM International Conference on Information and Knowledge Management, pp. 403–410 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, MS., Chen, CP., Wang, HM. (2013). Query-Document Relevance Topic Models. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)