Implicit feedback for interactive information retrieval

Searchers can find the construction of query statements for submission to Information Retrieval (IR) systems a problematic activity. These problems are confounded by uncertainty about the information they are searching for, or an unfamiliarity with the retrieval system being used or collection being searched. On the World Wide Web these problems are potentially more acute as searchers receive little or no training in how to search effectively. Relevance feedback (RF) techniques allow searchers to directly communicate what information is relevant and help them construct improved query statements. However, the techniques require explicit relevance assessments that intrude on searchers’ primary lines of activity and as such, searchers may be unwilling to provide this feedback. Implicit feedback systems are unobtrusive and make inferences of what is relevant based on searcher interaction. They gather information to better represent searcher needs whilst minimising the burden of explicitly reformulating queries or directly providing relevance information. In this thesis I investigate implicit feedback techniques for interactive information retrieval. The techniques proposed aim to increase the quality and quantity of searcher interaction and use this interaction to infer searcher interests. I develop search interfaces that use representations of the top-ranked retrieved documents such as sentences and summaries to encourage a deeper examination of search results and drive the information seeking process. Implicit feedback frameworks based on heuristic and probabilistic approaches are described. These frameworks use interaction to identify needs and estimate changes in these needs during a search. The evidence gathered is used to modify search queries and make new search decisions such as re-searching the document collection or restructuring already retrieved information. The term selection models from the frameworks and elsewhere are evaluated using a simulation-based evaluation methodology that allows different search scenarios to be modelled. Findings show that the probabilistic term selection model generated the most effective search queries and learned what was relevant in the shortest time. Different versions of an interface that implements the probabilistic framework are evaluated to test it with human subjects and investigate how much control they want over its decisions. The experiment involved 48 subjects with different skill levels and search experience. The results show that searchers are happy to delegate responsibility to RF systems for relevance assessment (through implicit feedback), but not more severe search decisions such as formulating queries or selecting retrieval strategies. Systems that help searchers make these decisions are preferred to those that act directly on their behalf or await searcher action

Evaluating the implicit feedback models for adaptive video retrieval

Interactive video retrieval systems are becoming popular. On the one hand, these systems try to reduce the effect of the semantic gap, an issue currently being addressed by the multimedia retrieval community. On the other hand, such systems enhance the quality of information seeking for the user by supporting query formulation and reformulation. Interactive systems are very popular in the textual retrieval domain. However, they are relatively unexplored in the case of multimedia retrieval. The main problem in the development of interactive retrieval systems is the evaluation cost.The traditional evaluation methodology, as used in the information retrieval domain, is not applicable. An alternative is to use a user-centred evaluation methodology. However, such schemes are expensive in terms of effort, cost and are not scalable. This problem gets exacerbated by the use of implicit indicators, which are useful and increasingly used in predicting user intentions. In this paper, we explore the effectiveness of a number of interfaces and feedback mechanisms and compare their relative performance using a simulated evaluation methodology. The results show the relatively better performance of a search interface with the combination of explicit and implicit features

Search trails using user feedback to improve video search

In this paper we present an innovative approach for aiding users in the difficult task of video search. We use community based feedback mined from the interactions of previous users of our video search system to aid users in their search tasks. This feedback is the basis for providing recommendations to users of our video retrieval system. The ultimate goal of this system is to improve the quality of the results that users find, and in doing so, help users to explore a large and difficult information space and help them consider search options that they may not have considered otherwise. In particular we wish to make the difficult task of search for video much easier for users. The results of a user evaluation indicate that we achieved our goals, the performance of the users in retrieving relevant videos improved, and users were able to explore the collection to a greater extent

Studying Interaction Methodologies in Video Retrieval

So far, several approaches have been studied to bridge the problem of the Semantic Gap, the bottleneck in image and video retrieval. However, no approach is successful enough to increase retrieval performances significantly. One reason is the lack of understanding the user's interest, a major condition towards adapting results to a user. This is partly due to the lack of appropriate interfaces and the missing knowledge of how to interpret user's actions with these interfaces. In this paper, we propose to study the importance of various implicit indicators of relevance. Furthermore, we propose to investigate how this implicit feedback can be combined with static user profiles towards an adaptive video retrieval model

The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

A study of interface support mechanisms for interactive information retrieval

Advances in search technology have meant that search systems can now offer assistance to users beyond simply retrieving a set of documents. For example, search systems are now capable of inferring user interests by observing their interaction, offering suggestions about what terms could be used in a query, or reorganizing search results to make exploration of retrieved material more effective. When providing new search functionality, system designers must decide how the new functionality should be offered to users. One major choice is between (a) offering automatic features that require little human input, but give little human control; or (b) interactive features which allow human control over how the feature is used, but often give little guidance over how the feature should be best used. This article presents a study in which we empirically investigate the issue of control by presenting an experiment in which participants were asked to interact with three experimental systems that vary the degree of control they had in creating queries, indicating which results are relevant in making search decisions. We use our findings to discuss why and how the control users want over search decisions can vary depending on the nature of the decisions and the impact of those decisions on the user's search

A study of factors affecting the utility of implicit relevance feedback

Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it performs well and when it performs poorly. In this paper we investigate how the use and effectiveness of IRF is affected by three factors: search task complexity, the search experience of the user and the stage in the search. Our findings suggest that all three of these factors contribute to the utility of IRF

Evaluating implicit feedback models using searcher simulations

In this article we describe an evaluation of relevance feedback (RF) algorithms using searcher simulations. Since these algorithms select additional terms for query modification based on inferences made from searcher interaction, not on relevance information searchers explicitly provide (as in traditional RF), we refer to them as implicit feedback models. We introduce six different models that base their decisions on the interactions of searchers and use different approaches to rank query modification terms. The aim of this article is to determine which of these models should be used to assist searchers in the systems we develop. To evaluate these models we used searcher simulations that afforded us more control over the experimental conditions than experiments with human subjects and allowed complex interaction to be modeled without the need for costly human experimentation. The simulation-based evaluation methodology measures how well the models learn the distribution of terms across relevant documents (i.e., learn what information is relevant) and how well they improve search effectiveness (i.e., create effective search queries). Our findings show that an implicit feedback model based on Jeffrey's rule of conditioning outperformed other models under investigation

Comparing explicit and implicit feedback techniques for web retrieval : TREC-10 interactive track report

In this paper we examine the extent to which implicit feedback (where the system attempts to estimate what the user may be interested in) can act as a substitute for explicit feedback (where searchers explicitly mark documents relevant). Therefore, we attempt to side-step the problem of getting users to explicitly mark documents relevant by making predictions on relevance through analysing the user's interaction with the system. Specifically, we hypothesised that implicit and explicit feedback were interchangeable as sources of relevance information for relevance feedback. Through developing a system that utilised each type of feedback we were able to compare the two approaches in terms of search effectiveness