research-article

An optimization framework for query recommendation

Authors:

Aris Anagnostopoulos,

Luca Becchetti,

Carlos Castillo,

Aristides GionisAuthors Info & Claims

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

Pages 161 - 170

https://doi.org/10.1145/1718487.1718508

Published: 04 February 2010 Publication History

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Abstract

Query recommendation is an integral part of modern search engines. The goal of query recommendation is to facilitate users while searching for information. Query recommendation also allows users to explore concepts related to their information needs.

In this paper, we present a formal treatment of the problem of query recommendation. In our framework we model the querying behavior of users by a probabilistic reformula- tion graph, or query-flow graph [Boldi et al. CIKM 2008]. A sequence of queries submitted by a user can be seen as a path on this graph. Assigning score values to queries allows us to define suitable utility functions and to consider the expected utility achieved by a reformulation path on the query-flow graph. Providing recommendations can be seen as adding shortcuts in the query-flow graph that "nudge" the reformulation paths of users, in such a way that users are more likely to follow paths with larger expected utility.

We discuss in detail the most important questions that arise in the proposed framework. In particular, we provide examples of meaningful utility functions to optimize, we discuss how to estimate the effect of recommendations on the reformulation probabilities, we address the complexity of the optimization problems that we consider, we suggest efficient algorithmic solutions, and we validate our models and algorithms with extensive experimentation. Our techniques can be applied to other scenarios where user behavior can be modeled as a Markov process.

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Index Terms

An optimization framework for query recommendation
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

February 2010

468 pages

ISBN:9781605588896

DOI:10.1145/1718487

General Chairs:
Brian D. Davison
Lehigh University, USA
,
Torsten Suel
Polytechnic Institute of NYU, USA
,
Program Chairs:
Nick Craswell
Microsoft, USA
,
Bing Liu
University of Illinois, Chicago, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 February 2010

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WSDM'10: Third ACM International Conference on Web Search and Data Mining

February 4 - 6, 2010

New York, New York, USA

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