Search (6 results, page 1 of 1)

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Abstract

As a significant contextual factor in information search, topic knowledge has been gaining increased research attention. We report on a study of the relationship between information searchers' topic knowledge and their search behaviors, and on an attempt to predict searchers' topic knowledge from their behaviors during the search. Data were collected in a controlled laboratory experiment with 32 undergraduate journalism student participants, each searching on 4 tasks of different types. In general, behavioral variables were not found to have significant differences between users with high and low levels of topic knowledge, except the mean first dwell time on search result pages. Several models were built to predict topic knowledge using behavioral variables calculated at 3 different stages of search episodes: the first-query-round, the middle point of the search, and the end point. It was found that a model using some search behaviors observed in the first query round led to satisfactory prediction results. The results suggest that early-session search behaviors can be used to predict users' topic knowledge levels, allowing personalization of search for users with different levels of topic knowledge, especially in order to assist users with low topic knowledge.

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Abstract

It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.

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Abstract

User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.

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Abstract

Purpose - With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach - The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings - Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value - Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

Date

20. 1.2015 18:30:22

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Abstract

This article reports on a study that aimed at simplifying a search task difficulty reason scheme. Liu, Kim, and Creel (2015) (denoted LKC15) developed a 21-item search task difficulty reason scheme using a controlled laboratory experiment. The current study simplified the scheme through another experiment that followed the same design as LKC15 and involved 32 university students. The study had one added questionnaire item that provided a list of the 21 difficulty reasons in the multiple-choice format. By comparing the current study with LKC15, a concept of primary top difficulty reasons was proposed, which reasonably simplified the 21-item scheme to an 8-item top reason list. This limited number of reasons is more manageable and makes it feasible for search systems to predict task difficulty reasons from observable user behaviors, which builds the basis for systems to improve user satisfaction based on predicted search difficulty reasons.

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Abstract

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. This study looks at users' dwelling behavior on documents and several contextual factors: the stage of users' work tasks, task type, and users' knowledge of task topics, to explore whether or not taking account contextual factors could help infer document usefulness from dwell time. A controlled laboratory experiment was conducted with 24 participants, each coming 3 times to work on 3 subtasks in a general work task. The results show that task stage could help interpret certain types of dwell time as reliable indicators of document usefulness in certain task types, as was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how dwell time can be used as implicit evidence of document usefulness, as well as how contextual factors can help interpret dwell time as an indicator of usefulness. These findings have both theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems.