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 reformulation 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. Copyright 2010 ACM.

An optimization framework for query recommendation / Anagnostopoulos, Aristidis; Becchetti, Luca; Carlos, Castillo; Aristides, Gionis. - (2010), pp. 161-170. (Intervento presentato al convegno 3rd ACM International Conference on Web Search and Data Mining, WSDM 2010 tenutosi a New York City; United States nel 3 February 2010 through 6 February 2010) [10.1145/1718487.1718508].

An optimization framework for query recommendation

ANAGNOSTOPOULOS, ARISTIDIS;BECCHETTI, Luca;
2010

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 reformulation 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. Copyright 2010 ACM.
2010
3rd ACM International Conference on Web Search and Data Mining, WSDM 2010
query reformulations; query suggestions
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
An optimization framework for query recommendation / Anagnostopoulos, Aristidis; Becchetti, Luca; Carlos, Castillo; Aristides, Gionis. - (2010), pp. 161-170. (Intervento presentato al convegno 3rd ACM International Conference on Web Search and Data Mining, WSDM 2010 tenutosi a New York City; United States nel 3 February 2010 through 6 February 2010) [10.1145/1718487.1718508].
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