Search Results

User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and informational queries as a starting ...

Recently, unbiased learning-to-rank models have been widely studied to learn a better ranker by eliminating the biases from click data. Toward this goal, existing work mainly focused on estimating the propensity weight to design a specific bias type ...

One fundamental problem in image search is to learn the ranking functions (i.e., the similarity between query and image). Recent progress on this topic has evolved through two paradigms: the text-based model and image ranker learning. The former relies ...

We consider the problem of ranking rich verticals, which we call "cards," for a given mobile search query. Examples of card types include "SHOP" (showing access and contact information of a shop), "WEATHER" (showing a weather forecast for a particular ...

With the explosive growth of information available in the Web, locating needed and relevant information remains a difficult task, whether the information is textual or visual. Although information retrieval techniques have improved a lot in providing ...

Supporting exploratory search is a very challenging problem, not least because of the dynamic nature of the exercise: both the knowledge and interests of the user are subject to constant change. Moreover, whether the results for a query are informative ...

This work targets image retrieval task hold by MSR-Bing Grand Challenge. Image retrieval is considered as a challenge task because of the gap between low-level image representation and high-level textual query representation. Recently further developed ...

WSCD 2014 is the fourth workshop on Web Search Click Data, following WSCD 2009, WSCD 2011 and WSCD 2012. It is a forum for new research relating to Web search usage logs and for discussing desirable properties of publicly released search log datasets. ...

Web search components such as ranking and query suggestions analyze the user data provided in query and click logs. While this data is easy to collect and provides information about user behavior, it omits user interactions with the search engine that do ...

WSCD2012 is the second workshop on Web Search Click Data, following WSCD2009. It is a forum for new research relating to Web search usage logs and for discussing desirable properties of publicly released search log datasets. Research relating to search ...

Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully ...

Click data captures many users' document preferences for a query and has been shown to help significantly improve search engine ranking. However, most click data is noisy and of low frequency, with queries associated to documents via only one or a few ...

Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a ...

Capturing the "aboutness" of documents has been a key research focus throughout the history of automated textual information processing. In this work, we represent aboutness using words and phrases that best reflect the central topics of a document. We ...

Recently a number of studies have demonstrated that search engine logfiles are an important resource to determine the relevance relation between URLs and query terms. We hypothesized that the queries associated with a URL could also be presented as ...

We propose to use the search log to study long queries, in order to understand the types of information needs that are behind them, and to design techniques to improve search effectiveness when they are used. Long queries arise in many different ...

A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use this behavior graph for classification. We choose a click graph sampled from ...

Search engine click logs provide an invaluable source of relevance information, but this information is biased. A key source of bias is presentation order: the probability of click is influenced by a document's position in the results page. This paper ...

Search engines can record which documents were clicked for which query, and use these query-document pairs as "soft" relevance judgments. However, compared to the true judgments, click logs give noisy and sparse relevance information. We apply a Markov ...