Abstract
Query auto-completion (QAC) facilitates query formulation by predicting completions for given query prefix inputs. Most web search engines use behavioral signals to customize query completion lists for users. To be effective, such personalized QAC models rely on the access to sufficient context about each user’s interest and intentions. Hence, they often suffer from data sparseness problems. For this reason, we propose the construction and application of cohorts to address context sparsity and to enhance QAC personalization. We build an individual’s interest profile by learning his/her topic preferences through topic models and then aggregate users who share similar profiles. As conventional topic models are unable to automatically learn cohorts, we propose two cohort topic models that handle topic modeling and cohort discovery in the same framework. We present four cohort-based personalized QAC models that employ four different cohort discovery strategies. Our proposals use cohorts’ contextual information together with query frequency to rank completions. We perform extensive experiments on the publicly available AOL query log and compare the ranking effectiveness with that of models that discard cohort contexts. Experimental results suggest that our cohort-based personalized QAC models can solve the sparseness problem and yield significant relevance improvement over competitive baselines.
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References
Bar-Yossef Z, Kraus N, 2011. Context-sensitive query auto-completion. Proc 20th Int Conf on World Wide Web, p.107–116. https://doi.org/10.1145/1963405.1963424
Blei DM, Ng AY, Jordan MI, 2003. Latent Dirichlet allocation. J Mach Learn Res, 3:993–1022.
Burges CJC, Svore KM, Bennett PN, et al., 2011. Learning to rank using an ensemble of lambda-gradient models. J Mach Learn Res, 14:25–35.
Cai F, de Rijke M, 2016a. Learning from homologous queries and semantically related terms for query auto completion. Inform Process Manag, 52(4):628–643. https://doi.org/10.1016/j.ipm.2015.12.008
Cai F, de Rijke M, 2016b. A survey of query auto completion in information retrieval. Found Trends Inform Retr, 10(4):273–363. https://doi.org/10.1561/1500000055
Cai F, Reinanda R, de Rijke M, 2016a. Diversifying query auto-completion. ACM Trans Inform Syst, 34(4), Artilce 25. https://doi.org/10.1145/2910579
Cai F, Liang SS, de Rijke M, 2016b. Prefix-adaptive and time-sensitive personalized query auto completion. IEEE Trans Knowl Data Eng, 28(9):2452–2466. https://doi.org/10.1109/TKDE.2016.2568179
Chen X, Zhou MY, Carin L, 2012. The contextual focused topic model. Proc 18th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.96–104. https://doi.org/10.1145/2339530.2339549
Fiorini N, Lu ZY, 2018. Personalized neural language models for real-world query auto completion. 16th Annual Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.208–215. https://doi.org/10.18653/v1/N18-3026
Gama J, Žliobaitė I, Bifet A, et al., 2014. A survey on concept drift adaptation. ACM Comput Surv, 46(4), Article 44. https://doi.org/10.1145/2523813
Hassan A, White RW, 2013. Personalized models of search satisfaction. Proc 22nd ACM Int Conf on Information and Knowledge Management, p.2009–2018. https://doi.org/10.1145/2505515.2505681
Jiang JY, Cheng PJ, 2016. Classifying user search intents for query auto-completion. Proc ACM Int Conf on the Theory of Information Retrieval, p.49–58. https://doi.org/10.1145/2970398.2970400
Li DF, Ding Y, Shuai X, et al., 2012. Adding community and dynamic to topic models. J Inform, 6(2):237–253. https://doi.org/10.1016/j.joi.2011.11.004
Li LD, Deng HB, Dong AL, et al., 2017a. Exploring query auto-completion and click logs for contextual-aware web search and query suggestion. Proc 26th Int Conf on World Wide Web, p.539–548. https://doi.org/10.1145/3038912.3052593
Li LD, Deng HB, Chen JH, et al., 2017b. Learning parametric models for context-aware query auto-completion via Hawkes processes. Proc 10th ACM Int Conf on Web Search and Data Mining, p.131–139. https://doi.org/10.1145/3018661.3018698
Mitra B, 2015. Exploring session context using distributed representations of queries and reformulations. Proc 38th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.3–12. https://doi.org/10.1145/2766462.2767702
Mitra B, Shokouhi M, Radlinski F, et al., 2014. On user interactions with query auto-completion. Proc 37th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1055–1058. https://doi.org/10.1145/2600428.2609508
Morris MR, Horvitz E, 2007. Searchtogether: an interface for collaborative web search. Proc 20th Annual ACM Symp on User Interface Software and Technology, p.3–12. https://doi.org/10.1145/1294211.1294215
Neiswanger W, Wang C, Xing E, 2014. Asymptotically exact, embarrassingly parallel MCMC. Proc 13th Conf on Uncertainty in Artificial Intelligence, p.623–632. http://arxiv.org/abs/1311.4780v2
Park DH, Chiba R, 2017. A neural language model for query auto-completion. Proc 40th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1189–1192. https://doi.org/10.1145/3077136.3080758
Pass G, Chowdhury A, Torgeson C, 2006. A picture of search. Proc 1st Int Conf on Scalable Information Systems, p.1. https://doi.org/10.1145/1146847.1146848
Shokouhi M, 2013. Learning to personalize query auto-completion. Proc 36th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.103–112. https://doi.org/10.1145/2484028.2484076
Shokouhi M, Radinsky K, 2012. Time-sensitive query auto-completion. Proc 35th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.601–610. https://doi.org/10.1145/2348283.2348364
Smyth B, Balfe E, Briggs P, et al., 2003. Collaborative web search. Proc 18th Int Joint Conf on Artificial Intelligence, p.1417–1419.
Steyvers M, Smyth P, Rosen-Zvi M, et al., 2004. Probabilistic author-topic models for information discovery. Proc 10th Int Conf on Knowledge Discovery and Data Mining, p.306–315. https://doi.org/10.1145/1014052.1014087
Teevan J, Morris MR, Bush S, 2009. Discovering and using groups to improve personalized search. Proc 2nd ACM Int Conf on Web Search and Data Mining, p.15–24. https://doi.org/10.1145/1498759.1498786
White RW, Chu W, Hassan A, et al., 2013. Enhancing personalized search by mining and modeling task behavior. Proc 22nd Int Conf on World Wide Web, p.1411–1420. https://doi.org/10.1145/2488388.2488511
Wu XD, Kumar V, Quinlan JR, et al., 2008. Top 10 algorithms in data mining. Knowl Inform Syst, 14(1):1–37. https://doi.org/10.1007/s10115-007-0114-2
Yan JY, Chu W, White RW, 2014. Cohort modeling for enhanced personalized search. Proc 37th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.505–514. https://doi.org/10.1145/2600428.2609617
Yin ZJ, Cao LL, Gu QQ, et al., 2012. Latent community topic analysis: integration of community discovery with topic modeling. ACM Trans Intell Syst Technol, 3(4), Article 63. https://doi.org/10.1145/2337542.2337548
Zhang A, Goyal A, Kong WZ, et al., 2015. adaQAC: adaptive query auto-completion via implicit negative feedback. Proc 38th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.143–152. https://doi.org/10.1145/2766462.2767697
Zheng GQ, Guo JW, Yang LC, et al., 2011. Mining topics on participations for community discovery. Proc 34th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.445–454. https://doi.org/10.1145/2009916.2009977
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Dan-yang JIANG and Hong-hui CHEN declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. 61702526), the Defense Industrial Technology Development Program of China (No. JCKY2017204B064), and the National Advanced Research Project of China (No. 6141B0801010b)
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Jiang, Dy., Chen, Hh. Cohort-based personalized query auto-completion. Frontiers Inf Technol Electronic Eng 20, 1246–1258 (2019). https://doi.org/10.1631/FITEE.1800010
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DOI: https://doi.org/10.1631/FITEE.1800010