ABSTRACT
Query auto-completion (QAC) facilitates user query composition by suggesting queries given query prefix inputs. In 2014, global users of Yahoo! Search saved more than 50% keystrokes when submitting English queries by selecting suggestions of QAC. Users' preference of queries can be inferred during user-QAC interactions, such as dwelling on suggestion lists for a long time without selecting query suggestions ranked at the top. However, the wealth of such implicit negative feedback has not been exploited for designing QAC models. Most existing QAC models rank suggested queries for given prefixes based on certain relevance scores.
We take the initiative towards studying implicit negative feed- back during user-QAC interactions. This motivates re-designing QAC in the more general "(static) relevance"(adaptive) implicit negative feedback? framework. We propose a novel adaptive model adaQAC that adapts query auto-completion to users' implicit negative feedback towards unselected query suggestions. We collect user-QAC interaction data and perform large-scale experiments. Empirical results show that implicit negative feedback significantly and consistently boosts the accuracy of the investigated static QAC models that only rely on relevance scores. Our work compellingly makes a key point: QAC should be designed in a more general framework for adapting to implicit negative feedback.
- E. Adar, D. S. Weld, B. N. Bershad, and S. S. Gribble. Why we search: visualizing and predicting user behavior. In WWW, 2007. Google ScholarDigital Library
- Z. Bar-Yossef and N. Kraus. Context-sensitive query auto-completion. In WWW, 2011. Google ScholarDigital Library
- C. M. Bishop. Pattern recognition and machine learning, volume 1. 2006. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe. Convex optimization. 2009. Google ScholarDigital Library
- F. Cai, S. Liang, and M. de Rijke. Time-sensitive personalized query auto-completion. In CIKM, 2014. Google ScholarDigital Library
- S. Chaudhuri and R. Kaushik. Extending autocompletion to tolerate errors. In SIGMOD, 2009. Google ScholarDigital Library
- T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to algorithms, volume 2. 2001. Google ScholarDigital Library
- H. Duan and B.-J. P. Hsu. Online spelling correction for query completion. In WWW, 2011. Google ScholarDigital Library
- D. Guan, S. Zhang, and H. Yang. Utilizing query change for session search. In SIGIR, 2013. Google ScholarDigital Library
- T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning, volume 2. 2009.Google ScholarCross Ref
- K. Hofmann, B. Mitra, F. Radlinski, and M. Shokouhi. An eye-tracking study of user interactions with query auto completion. In CIKM, 2014. Google ScholarDigital Library
- Y. Hong, Q. Cai, S. Hua, J. Yao, and Q. Zhu. Negative feedback: the forsaken nature available for re-ranking. In COLING, 2010. Google ScholarDigital Library
- B.-J. P. Hsu and G. Ottaviano. Space-efficient data structures for top-k completion. In WWW, 2013. Google ScholarDigital Library
- S. Ji, G. Li, C. Li, and J. Feng. Efficient interactive fuzzy keyword search. In WWW, 2009. Google ScholarDigital Library
- J.-Y. Jiang, Y.-Y. Ke, P.-Y. Chien, and P.-J. Cheng. Learning user reformulation behavior for query auto-completion. In SIGIR, 2014. Google ScholarDigital Library
- S. R. Kairam, M. R. Morris, J. Teevan, D. J. Liebling, and S. T. Dumais. Towards supporting search over trending events with social media. In ICWSM, 2013.Google Scholar
- M. Karimzadehgan and C. Zhai. Improving retrieval accuracy of difficult queries through generalizing negative document language models. In CIKM, 2011. Google ScholarDigital Library
- E. Kharitonov, C. Macdonald, P. Serdyukov, and I. Ounis. User model-based metrics for offline query suggestion evaluation. In SIGIR, 2013. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009. Google ScholarDigital Library
- A. Kulkarni, J. Teevan, K. M. Svore, and S. T. Dumais. Understanding temporal query dynamics. In WSDM, 2011. Google ScholarDigital Library
- Y. Li, A. Dong, H. Wang, H. Deng, Y. Chang, and C. Zhai. A two-dimensional click model for query auto-completion. In SIGIR, 2014. Google ScholarDigital Library
- Y. Li, X. Tao, A. Algarni, and S.-T. Wu. Mining specific and general features in both positive and negative relevance feedback. In TREC, 2009.Google Scholar
- J. Luo, S. Zhang, and H. Yang. Win-win search: Dual-agent stochastic game in session search. In SIGIR, 2014. Google ScholarDigital Library
- Y. Ma and H. Lin. A multiple relevance feedback strategy with positive and negative models. PloS ONE, 9(8), 2014.Google Scholar
- C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. 2008. Google ScholarCross Ref
- B. Mitra, M. Shokouhi, F. Radlinski, and K. Hofmann. On user interactions with query auto-completion. In SIGIR, 2014. Google ScholarDigital Library
- T. Miyanishi and T. Sakai. Time-aware structured query suggestion. In SIGIR, 2013. Google ScholarDigital Library
- K. P. Murphy. Machine learning: a probabilistic perspective. 2012. Google ScholarDigital Library
- A. Nedić. Optimization. Technical Report, UIUC, 2011.Google Scholar
- J. J. Rocchio. Relevance feedback in information retrieval. The SMART Retrieval System Experiments in Automatic Document Processing, 1971. Google ScholarDigital Library
- M. Shokouhi. Learning to personalize query auto-completion. In SIGIR, 2013. Google ScholarDigital Library
- M. Shokouhi and K. Radinsky. Time-sensitive query auto-completion. In SIGIR, 2012. Google ScholarDigital Library
- X. Wang, H. Fang, and C. Zhai. Improve retrieval accuracy for difficult queries using negative feedback. In CIKM, 2007. Google ScholarDigital Library
- X. Wang, H. Fang, and C. Zhai. A study of methods for negative relevance feedback. In SIGIR, 2008. Google ScholarDigital Library
- S. Whiting and J. M. Jose. Recent and robust query auto-completion. In WWW, 2014. Google ScholarDigital Library
- C. Xiao, J. Qin, W. Wang, Y. Ishikawa, K. Tsuda, and K. Sadakane. Efficient error-tolerant query autocompletion. VLDB, 6(6), 2013. Google ScholarDigital Library
- H. Yang, M. Sloan, and J. Wang. Dynamic information retrieval modeling. In SIGIR, 2014. Google ScholarDigital Library
- S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In SIGIR, 2011. Google ScholarDigital Library
- W. Zhang and J. Wang. The study of methods for language model based positive and negative relevance feedback in information retrieval. In ISISE, 2012. Google ScholarDigital Library
Index Terms
- adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback
Recommendations
Query Auto-Completion for Rare Prefixes
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementQuery auto-completion (QAC) systems typically suggest queries that have previously been observed in search logs. Given a partial user query, the system looks up this query prefix against a precomputed set of candidates, then orders them using ranking ...
Analyzing User's Sequential Behavior in Query Auto-Completion via Markov Processes
SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information RetrievalQuery auto-completion (QAC) plays an important role in assisting users typing less while submitting a query. The QAC engine generally offers a list of suggested queries that start with a user's input as a prefix, and the list of suggestions is changed ...
Learning to personalize query auto-completion
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrievalQuery auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes ...
Comments