NDLI: Context-sensitive query auto-completion

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Context-sensitive query auto-completion

Content Provider	ACM Digital Library
Author	Kraus, Naama Bar-Yossef, Ziv
Abstract	Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query. In order to evaluate our approach, we performed extensive experimentation over the public AOL query log. We demonstrate that when the recent user's queries are relevant to the current query she is typing, then after typing a single character, NearestCompletion's MRR is 48% higher relative to the MRR of the standard MostPopularCompletion algorithm on average. When the context is irrelevant, however, NearestCompletion's MRR is essentially zero. To mitigate this problem, we propose HybridCompletion, which is a hybrid of NearestCompletion with MostPopularCompletion. HybridCompletion is shown to dominate both NearestCompletion and MostPopularCompletion, achieving a total improvement of 31.5% in MRR relative to MostPopularCompletion on average.
Starting Page	107
Ending Page	116
Page Count	10
File Format	PDF
ISBN	9781450306324
DOI	10.1145/1963405.1963424
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query expansion Context-awareness Query auto-completion
Content Type	Text
Resource Type	Article

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