20,312 research outputs found
A survey on the use of relevance feedback for information access systems
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems
Combining and selecting characteristics of information use
In this paper we report on a series of experiments designed to investigate the combination of term and document weighting functions in Information Retrieval. We describe a series of weighting functions, each of which is based on how information is used within documents and collections, and use these weighting functions in two types of experiments: one based on combination of evidence for ad-hoc retrieval, the other based on selective combination of evidence within a relevance feedback situation. We discuss the difficulties involved in predicting good combinations of evidence for ad-hoc retrieval, and suggest the factors that may lead to the success or failure of combination. We also demonstrate how, in a relevance feedback situation, the relevance assessments can provide a good indication of how evidence should be selected for query term weighting. The use of relevance information to guide the combination process is shown to reduce the variability inherent in combination of evidence
Preliminary Experiments using Subjective Logic for the Polyrepresentation of Information Needs
According to the principle of polyrepresentation, retrieval accuracy may
improve through the combination of multiple and diverse information object
representations about e.g. the context of the user, the information sought, or
the retrieval system. Recently, the principle of polyrepresentation was
mathematically expressed using subjective logic, where the potential
suitability of each representation for improving retrieval performance was
formalised through degrees of belief and uncertainty. No experimental evidence
or practical application has so far validated this model. We extend the work of
Lioma et al. (2010), by providing a practical application and analysis of the
model. We show how to map the abstract notions of belief and uncertainty to
real-life evidence drawn from a retrieval dataset. We also show how to estimate
two different types of polyrepresentation assuming either (a) independence or
(b) dependence between the information objects that are combined. We focus on
the polyrepresentation of different types of context relating to user
information needs (i.e. work task, user background knowledge, ideal answer) and
show that the subjective logic model can predict their optimal combination
prior and independently to the retrieval process
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
- …