Prefix-adaptive and Time-sensitive Personalized Query Auto Completion?

Query auto completion (QAC) methods recommend queries to search engine users when they start entering a query. Current QAC methods mostly rank query completions based on their past popularity, i.e., on the number of times they have previously been submitted as a query. However, query popularity changes over time and may vary drastically across users. Accordingly, the ranking of query completions should be adjusted. Previous time-sensitive and user-specific QAC methods have been developed separately, yielding significant improvements over methods that are neither time-sensitive nor personalized. We propose a hybrid QAC method that is both time-sensitive and personalized. We extend it to handle long-tail prefixes, which we achieve by assigning optimal weights to the contribution from time-sensitivity and personalization. Using real-world search log datasets, we return top N query suggestions ranked by predicted popularity as estimated from popularity trends and cyclic popularity behavior; we rerank them by integrating similarities to a user’s previous queries (both in the current session and in previous sessions). Our method outperforms state-of-the-art time-sensitive QAC baselines, achieving total improvements of between 3% and 7% in terms of mean reciprocal rank (MRR). After optimizing the weights, our extended model achieves MRR improvements of between 4% and 8%.

  • Project Category : IEEE Projects
  • Project Year : 2016-2017
  • Department
  • B.E(Computer Science) ,
  • Domain
  • Data Mining,
  • Technology
  • ASP.Net, C#.Net,
  • Avilable city
  • Chennai, Pondicherry, Pune, Thanjavur,

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