Towards Mobile Query Auto-Completion: An Efficient Mobile Application-Aware Approach

We study the new mobile query auto-completion (QAC) problem to exploit mobile devices' exclusive signals, such as those related to mobile applications (apps). We propose AppAware, a novel QAC model using installed app and recently opened app signals to suggest queries for matching input prefixes on mobile devices. To overcome the challenge of noisy and voluminous signals, AppAware optimizes composite objectives with a lighter processing cost at a linear rate of convergence. We conduct experiments on a large commercial data set of mobile queries and apps. Installed app and recently opened app signals consistently and significantly boost the accuracy of various baseline QAC models on mobile devices.

[1]  Jiawei Han,et al.  adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback , 2015, SIGIR.

[2]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[3]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[4]  References , 1971 .

[5]  Tat-Seng Chua,et al.  New and improved: modeling versions to improve app recommendation , 2014, SIGIR.

[6]  Ricardo Baeza-Yates,et al.  Predicting The Next App That You Are Going To Use , 2015, WSDM.

[7]  Yurii Nesterov,et al.  Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.

[8]  Milad Shokouhi,et al.  Learning to personalize query auto-completion , 2013, SIGIR.

[9]  Ana Belén Barragáns-Martínez,et al.  Which App? A recommender system of applications in markets: Implementation of the service for monitoring users' interaction , 2012, Expert Syst. Appl..

[10]  Guoliang Li,et al.  Efficient interactive fuzzy keyword search , 2009, WWW '09.

[11]  Simon Moritz,et al.  Utilizing implicit feedback and context to recommend mobile applications from first use , 2011, CaRR '11.

[12]  Pu-Jen Cheng,et al.  Learning user reformulation behavior for query auto-completion , 2014, SIGIR.

[13]  Kunihiko Sadakane,et al.  Efficient Error-tolerant Query Autocompletion , 2013, Proc. VLDB Endow..

[14]  Y. Nesterov.  Gradient methods for minimizing composite objective function , 2007 .

[15]  Yidong Cui,et al.  A Probabilistic Top-N algorithm for mobile applications recommendation , 2013, 2013 5th IEEE International Conference on Broadband Network & Multimedia Technology.

[16]  Joemon M. Jose,et al.  Recent and robust query auto-completion , 2014, WWW.

[17]  Yang Song,et al.  Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance , 2013, WWW '13.

[18]  Huizhong Duan,et al.  Online spelling correction for query completion , 2011, WWW.

[19]  Tetsuya Sakai,et al.  Time-aware structured query suggestion , 2013, SIGIR.

[20]  Hongbo Deng,et al.  Analyzing User's Sequential Behavior in Query Auto-Completion via Markov Processes , 2015, SIGIR.

[21]  Hui Xiong,et al.  Mobile app recommendations with security and privacy awareness , 2014, KDD.

[22]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[23]  Shuang-Hong Yang,et al.  Collaborative competitive filtering: learning recommender using context of user choice , 2011, SIGIR.

[24]  Giuseppe Ottaviano,et al.  Space-efficient data structures for Top-k completion , 2013, WWW '13.

[25]  Filip Radlinski,et al.  On user interactions with query auto-completion , 2014, SIGIR.

[26]  Lei Cen,et al.  Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference , 2015, WSDM.

[27]  Surajit Chaudhuri,et al.  Extending autocompletion to tolerate errors , 2009, SIGMOD Conference.

[28]  Airu Yin,et al.  Interoperability-Enriched App Recommendation , 2014, ICDM Workshops.

[29]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[30]  M. de Rijke,et al.  Time-sensitive Personalized Query Auto-Completion , 2014, CIKM.

[31]  Wang-Chien Lee,et al.  App recommendation: a contest between satisfaction and temptation , 2013, WSDM.

[32]  Weiwei Deng,et al.  Mobile Application Recommendations Based on Complex Information , 2015, IEA/AIE.

[33]  Ricardo Baeza-Yates,et al.  A Study of Mobile Search Queries in Japan , 2007 .

[34]  Song Han,et al.  Query recommendation and its usefulness evaluation on mobile search engine , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[35]  Mark W. Schmidt,et al.  Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields , 2015, AISTATS.

[36]  Shi Wenxuan,et al.  Interoperability-Enriched App Recommendation , 2014, 2014 IEEE International Conference on Data Mining Workshop.

[37]  Philip S. Yu,et al.  On the Feature Discovery for App Usage Prediction in Smartphones , 2013, 2013 IEEE 13th International Conference on Data Mining.

[38]  Tong Zhang,et al.  Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.

[39]  Ya Xu,et al.  Computers and iphones and mobile phones, oh my!: a logs-based comparison of search users on different devices , 2009, WWW '09.

[40]  Hongbo Deng,et al.  A two-dimensional click model for query auto-completion , 2014, SIGIR.

[41]  James Allan,et al.  Predicting Search Intent Based on Pre-Search Context , 2015, SIGIR.

[42]  Bhaskar Mitra,et al.  An Eye-tracking Study of User Interactions with Query Auto Completion , 2014, CIKM.

[43]  Ming Xiao,et al.  Personalized mobile application discovery , 2014, CrowdSoft 2014.

[44]  Ziv Bar-Yossef,et al.  Context-sensitive query auto-completion , 2011, WWW.

[45]  Anindya Datta,et al.  Serendipitous Recommendation for Mobile Apps Using Item-Item Similarity Graph , 2013, AIRS.

[46]  Milad Shokouhi,et al.  Time-sensitive query auto-completion , 2012, SIGIR '12.