Search Results

In this paper we extend our previous work on strategies for automatically constructing noise resilient SVM detectors from click through data for large scale concept-based image retrieval. First, search log data is used in conjunction with Information ...

In the MSR-Bing Image Retrieval Challenge, the contestants are required to design a system that can score the query-image pairs based on the relevance between queries and images. To address this problem, we propose a regression based cross modal deep ...

Latent semantic models, such as LSA, intend to map a query to its relevant documents at the semantic level where keyword-based matching often fails. In this study we strive to develop a series of new latent semantic models with a deep structure that ...

In the online industry, user engagement is measured with various engagement metrics used to assess users' depth of engagement with a website. Widely-used metrics include clickthrough rates, page views and dwell time. Relying solely on these metrics can ...

Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical ...

Statistical translation models and latent semantic analysis (LSA) are two effective approaches to exploit click-through data for web search ranking. This paper presents two document ranking models that combine both approaches by explicitly modeling word-...

This article describes a brand new solution for classifying search results automatically basing on click through data. We introduce how to use specific association mining algorithm, feature selecting algorithm and SVM method to manage this ...

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus ...

Understanding the behavior of satisfied and unsatisfied Web search users is very important for improving users search experience. Collecting labeled data that characterizes search behavior is a very challenging problem. Most of the previous work used a ...

Online evaluation is amongst the few evaluation techniques available to the information retrieval community that is guaranteed to reflect how users actually respond to improvements developed by the community. Broadly speaking, online evaluation refers ...

This paper presents two new document ranking models for Web search based upon the methods of semantic representation and the statistical translation-based approach to information retrieval (IR). Assuming that a query is parallel to the titles of the ...

Various click models have been recently proposed as a principled approach to infer the relevance of documents from the clickthrough data. The inferred document relevance is potentially useful in evaluating the Web retrieval systems. In practice, it ...

Search engines are very effective in finding relevant pages for a query. When a query is ambiguous, the search engine returns a mix of results for different semantic interpretations of the query. This paper proposes a method to extract concepts from the ...

In this paper a novel approach for automatically annotating image databases is proposed. Despite most current approaches that are just based on spatial content analysis, the proposed method properly combines implicit feedback information and visual ...

Web search is challenging partly due to the fact that search queries and Web documents use different language styles and vocabularies. This paper provides a quantitative analysis of the language discrepancy issue, and explores the use of clickthrough ...

Search engines have been one of the most popular ways for people to find web pages of interest. Presently, when a user enters a keyword in a search engine, the search results are usually presented the same result to other users who search the same ...

We propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In particular, we draw conclusions on a document relevance by observing the user behavior after he ...

Current low-level feature-based CBIR methods do not provide meaningful results on non-annotated content. On the other hand manual annotation is both time/money consuming and user-dependent. To address these problems in this paper we present an automatic ...

The keyword based search technique suffers from the problem of synonymic and polysemic queries. Current approaches address only the problem of synonymic queries in which different queries might have the same information requirement. But the problem of ...

Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web search applications. Such benefits, however, are severely limited by the data ...