Search (6 results, page 1 of 1)

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Abstract

When domain novices are in C.C. Kuhlthau's (1993) Stage 3, the exploration stage of researching an assignment, they often do not know their information need; this causes them to go back to Stage 2, the topic-selection stage, when they are selecting keywords to formulate their query to an Information Retrieval (IR) system. Our hypothesis is that instead of going backward, they should be going forward toward a goal state-the performance of the task for which they are seeking the information. If they can somehow construct their goal state into a query, this forward-looking query better operationalizes their information need than does a topic-based query. For domain novice undergraduates seeking information for a course essay, we define their task as selecting a high-impact essay structure which will put the students' learning on display for the course instructor who will evaluate the essay. We report a study of first-year history undergraduate students which tested the use and effectiveness of "essay type" as a task-focused query-formulation device. We randomly assigned 78 history undergraduates to an intervention group and a control group. The dependent variable was essay quality, based on (a) an evaluation of the student's essay by a research team member, and (b) the marks given to the student's essay by the course instructor. We found that conscious or formal consideration of essay type is inconclusive as a basis of a task-focused query-formulation device for IR.

Type

a

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Abstract

The article reports a field study which examined the mental models of 80 undergraduates seeking information for either a history or psychology course essay when they were in an early, exploration stage of researching their essay. This group is presently at a disadvantage when using thesaurus-type schemes in indexes and online search engines because there is a disconnect between how domain novice users of IR systems represent a topic space and how this space is represented in the standard IR system thesaurus. The study attempted to (a) ascertain the coding language used by the 80 undergraduates in the study to mentally represent their topic and then (b) align the mental models with the hierarchical structure found in many thesauri. The intervention focused the undergraduates' thinking about their topic from a topic statement to a thesis statement. The undergraduates were asked to produce three mental model diagrams for their real-life course essay at the beginning, middle, and end of the interview, for a total of 240 mental model diagrams, from which we created a 12-category mental model classification scheme. Findings indicate that at the end of the intervention, (a) the percentage of vertical mental models increased from 24 to 35% of all mental models; but that (b) 3rd-year students had fewer vertical mental models than did 1st-year undergraduates in the study, which is counterintuitive. The results indicate that there is justification for pursuing our research based on the hypothesis that rotating a domain novice's mental model into a vertical position would make it easier for him or her to cognitively connect with the thesaurus's hierarchical representation of the topic area.

Type

a

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Abstract

We unpack the complexities of the crowdfunder community by identifying different archetypes of crowdfunders funding technology projects on Kickstarter.com. Drawing on the extant literature on innovation adoption and opinion leadership, we propose two dimensions of crowdfunders that capture the heterogeneity in crowdfunders' behavior: opinion leadership and interest specialization of crowdfunders. Using a set of variables representing these two dimensions, our analysis revealed five distinct archetypes of crowdfunders: the Vocal Actives, the Silent Actives, the Focused Enthusiasts, the Trend Followers, and the Star Seekers, who each adopted distinct crowdfunding strategies. We established external and criterion-related validity of the cluster solutions in multiple ways. Our results suggest that the composition of crowdfunders is complex, even within a single platform.

Type

a

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Abstract

The central issue in language model estimation is smoothing, which is a technique for avoiding zero probability estimation problem and overcoming data sparsity. There are three representative smoothing methods: Jelinek-Mercer (JM) method; Bayesian smoothing using Dirichlet priors (Dir) method; and absolute discounting (Dis) method, whose parameters are usually estimated empirically. Previous research in information retrieval (IR) on smoothing parameter estimation tends to select a single value from optional values for the collection, but it may not be appropriate for all the queries. The effectiveness of all the optional values should be considered to improve the ranking performance. Recently, learning to rank has become an effective approach to optimize the ranking accuracy by merging the existing retrieval methods. In this article, the smoothing methods for language modeling in information retrieval (LMIR) with different parameters are treated as different retrieval methods, then a learning to rank approach to learn a ranking model based on the features extracted by smoothing methods is presented. In the process of learning, the effectiveness of all the optional smoothing parameters is taken into account for all queries. The experimental results on the Learning to Rank for Information Retrieval (LETOR) LETOR3.0 and LETOR4.0 data sets show that our approach is effective in improving the performance of LMIR.

Type

a

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Abstract

In recent years, millions of households have shifted from traditional TVs to smart TVs for viewing online videos on TV screens. In this article, we perform extensive analyses on a large-scale online video viewing log on smart TVs. Because time influences almost every aspect of our lives, our aim is to understand temporal patterns of the online video viewing behavior of smart TV viewers at the crowd level. First, we measure the amount of time per hour spent in watching online videos on smart TV by each household on each day. By applying clustering techniques, we identify eight daily patterns whose peak hours occur in different segments of the day. The differences among households can be characterized by three types of temporal habits. We also uncover five periodic weekly patterns. There seems to be a circadian rhythm at the crow level. Further analysis confirms that there exists a holiday effect in the online video viewing behavior on smart TVs. Finally, we investigate the popularity variations of different video categories over the day. The obtained insights shed light on how we can partition a day to improve the performance of time-aware video recommendations for smart TV viewers.

Type

a

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Abstract

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state-of-the-art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Type

a