Recommending High Utility Queries via Query-Reformulation Graph (original) (raw)

An optimization framework for query recommendation

Proceedings of the third ACM international conference on Web search and data mining - WSDM '10, 2010

Query recommendations are an integral part of modern search engines. Their goal is to facilitate users' search tasks, as well as help them discover and 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 user-querying behavior by a probabilistic reformulation graph, or query-flow graph [Boldi et al. CIKM 2008], so that the 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 performing a random walk on the query-flow graph. Furthermore, 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.

IJERT-Suggesting Relevant Queries Based on Transition Probability of Information in Web Graphs

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/suggesting-relevant-queries-based-on-transition-probability-of-information-in-web-graphs https://www.ijert.org/research/suggesting-relevant-queries-based-on-transition-probability-of-information-in-web-graphs-IJERTV2IS111054.pdf Query suggestion is an important process in the case of a search engine to predict the user's information needs. In many cases, we can generate the relevant prediction from large scale Web graphs containing queries and other related information like clickthrough data generated by the search engine. However generating suggestion based on the semantic relevancy with the user's information need is a challenging problem. In this paper, a modification of the general query suggestion technique is proposed which is based on query-URL graph based on the clickthrough data generated by the search engine. Here the transition probability of information between nodes in the graph is taken as the parameter to suggest relevant queries. Based on this, a sub graph is constructed by short random walk on the graph and the basic heat diffusion equation is applied in the sub graph to suggest the relevant queries. Also the complexity of the existing query suggestion algorithm based on the heat diffusion equation is reduced by this approach.

Query suggestions using query-flow graphs

2009

The query-flow graph [Boldi et al., CIKM 2008] is an aggregated representation of the latent querying behavior contained in a query log. Intuitively, in the query-flow graph a directed edge from query qi to query qj means that the two queries are likely to be part of the same search mission. Any path over the query-flow graph may be seen as a possible search task, whose likelihood is given by the strength of the edges along the path. An edge (qi, qj) is also labelled with some information: e.g., the probability that user moves from qi to qj, or the type of the transition, for instance, the fact that qj is a specialization of qi.

A Probabilistic Query Suggestion Approach without Using Query Logs

2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013

Commercial web search engines include a query suggestion module so that given a user's keyword query, alternative suggestions are offered and served as a guide to assist the user in formulating queries which capture his/her intended information need in a quick and simple manner. Majority of these modules, however, perform an in-depth analysis of large query logs and thus (i) their suggestions are mostly based on queries frequently posted by users and (ii) their design methodologies cannot be applied to make suggestions on customized search applications for enterprises for which their respective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, a probabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, since it solely relies on the availability of user-generated content freely accessible online, such as the Wikipedia.org document collection, and applies simple, yet effective, probabilistic-and information retrieval-based models, i.e., the Multinomial, Bigram Language, and Vector Space Models, to provide useful and diverse query suggestions. Empirical studies conducted using a set of test queries and the feedbacks provided by Mechanical Turk appraisers have verified that PQS makes more useful suggestions than Yahoo! and is almost as good as Google and Bing based on the relatively small difference in performance measures achieved by Google and Bing over PQS.

Query Recommendation Using Query Logs in Search Engines

2004

In this paper we propose a method that, given a query submitted to a search engine, suggests a list of related queries. The related queries are based in previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The method proposed is based on a query clustering process in which groups of semantically similar queries are identified. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. The method not only discovers the related queries, but also ranks them according to a relevance criterion. Finally, we show with experiments over the query log of a search engine the effectiveness of the method.

Efficient query recommendations in the long tail via center-piece subgraphs

2012

Abstract We present a recommendation method based on the well-known concept of center-piece subgraph, that allows for the time/space efficient generation of suggestions also for rare, ie, long-tail queries. Our method is scalable with respect to both the size of datasets from which the model is computed and the heavy workloads that current web search engines have to deal with. Basically, we relate terms contained into queries with highly correlated queries in a query-flow graph.

Improving recommendation for long-tail queries via templates

2011

The ability to aggregate huge volumes of queries over a large population of users allows search engines to build precise models for a variety of query-assistance features such as query recommendation, correction, etc. Yet, no matter how much data is aggregated, the long-tail distribution implies that a large fraction of queries are rare. As a result, most query assistance services perform poorly or are not even triggered on long-tail queries. We propose a method to extend the reach of query assistance techniques (and in particular query recommendation) to long-tail queries by reasoning about rules between query templates rather than individual query transitions, as currently done in query-flow graph models. As a simple example, if we recognize that 'Montezuma' is a city in the rare query "Montezuma surf" and if the rule ' surf → beach' has been observed, we are able to offer "Montezuma beach" as a recommendation, even if the two queries were never observed in a same session. We conducted experiments to validate our hypothesis, first via traditional small-scale editorial assessments but more interestingly via a novel automated large scale evaluation methodology. Our experiments show that general coverage can be relatively increased by 24% using templates without penalizing quality. Furthermore, for 36% of the 95M queries in our query flow graph, which have no out edges and thus could not be served recommendations, we can now offer at least one recommendation in 98% of the cases.