5,627 research outputs found
Towards an automated query modification assistant
Users who need several queries before finding what they need can benefit from
an automatic search assistant that provides feedback on their query
modification strategies. We present a method to learn from a search log which
types of query modifications have and have not been effective in the past. The
method analyses query modifications along two dimensions: a traditional
term-based dimension and a semantic dimension, for which queries are enriches
with linked data entities. Applying the method to the search logs of two search
engines, we identify six opportunities for a query modification assistant to
improve search: modification strategies that are commonly used, but that often
do not lead to satisfactory results.Comment: 1st International Workshop on Usage Analysis and the Web of Data
(USEWOD2011) in the 20th International World Wide Web Conference (WWW2011),
Hyderabad, India, March 28th, 201
Analysing Web Multimedia Query Reformulation Behaviour
Current multimedia Web search engines still use keywords as the primary means to search. Due to the richness in multimedia contents, general users constantly experience some difficulties in formulating textual queries that are representative enough for their needs. As a result, query reformulation becomes part of an inevitable process in most multimedia searches. Previous Web query formulation studies did not investigate the modification sequences and thus can only report limited findings on the reformulation behavior. In this study, we propose an automatic approach to examine multimedia query reformulation using large-scale transaction logs. The key findings show that search term replacement is the most dominant type of modifications in visual searches but less important in audio searches. Image search users prefer the specified search strategy more than video and audio users. There is also a clear tendency to replace terms with synonyms or associated terms in visual queries. The analysis of the search strategies in different types of multimedia searching provides some insights into userās searching behavior, which can contribute to the design of future query formulation assistance for keyword-based Web multimedia retrieval systems
DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users
The investigation of the browsing behavior of users provides useful
information to optimize web site design, web browser design, search engines
offerings, and online advertisement. This has been a topic of active research
since the Web started and a large body of work exists. However, new online
services as well as advances in Web and mobile technologies clearly changed the
meaning behind "browsing the Web" and require a fresh look at the problem and
research, specifically in respect to whether the used models are still
appropriate. Platforms such as YouTube, Netflix or last.fm have started to
replace the traditional media channels (cinema, television, radio) and media
distribution formats (CD, DVD, Blu-ray). Social networks (e.g., Facebook) and
platforms for browser games attracted whole new, particularly less tech-savvy
audiences. Furthermore, advances in mobile technologies and devices made
browsing "on-the-move" the norm and changed the user behavior as in the mobile
case browsing is often being influenced by the user's location and context in
the physical world. Commonly used datasets, such as web server access logs or
search engines transaction logs, are inherently not capable of capturing the
browsing behavior of users in all these facets. DOBBS (DERI Online Behavior
Study) is an effort to create such a dataset in a non-intrusive, completely
anonymous and privacy-preserving way. To this end, DOBBS provides a browser
add-on that users can install, which keeps track of their browsing behavior
(e.g., how much time they spent on the Web, how long they stay on a website,
how often they visit a website, how they use their browser, etc.). In this
paper, we outline the motivation behind DOBBS, describe the add-on and captured
data in detail, and present some first results to highlight the strengths of
DOBBS
Characterization of portuguese web searches
Tese de mestrado integrado. Engenharia InformĆ”tica e ComputaĆ§Ć£o. Universidade do Porto. Faculdade de Engenharia. 201
Examining repetition in user search behavior
This paper describes analyses of the repeated use of search engines.
It is shown that users commonly re-issue queries, either to examine search
results deeply or simply to query again, often days or weeks later. Hourly and
weekly periodicities in behavior are observed for both queries and clicks.
Navigational queries were found to be repeated differently from others
Application of the Markov Chain Method in a Health Portal Recommendation System
This study produced a recommendation system that can effectively recommend items on a health portal. Toward this aim, a transaction log that records usersā traversal activities on the Medical College of Wisconsinās HealthLink, a health portal with a subject directory, was utilized and investigated. This study proposed a mixed-method that included the transaction log analysis method, the Markov chain analysis method, and the inferential analysis method. The transaction log analysis method was applied to extract usersā traversal activities from the log. The Markov chain analysis method was adopted to model usersā traversal activities and then generate recommendation lists for topics, articles, and Q&A items on the health portal. The inferential analysis method was applied to test whether there are any correlations between recommendation lists generated by the proposed recommendation system and recommendation lists ranked by experts. The topics selected for this study are Infections, the Heart, and Cancer. These three topics were the three most viewed topics in the portal. The findings of this study revealed the consistency between the recommendation lists generated from the proposed system and the lists ranked by experts. At the topic level, two topic recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while one topic recommendation list was highly consistent with the list ranked by experts. At the article level, one article recommendation list generated from the proposed system was consistent with the list ranked by experts, while 14 article recommendation lists were highly consistent with the lists ranked by experts. At the Q&A item level, three Q&A item recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while 12 Q&A item recommendation lists were highly consistent with the lists ranked by experts. The findings demonstrated the significance of usersā traversal data extracted from the transaction log. The methodology applied in this study proposed a systematic approach to generating the recommendation systems for other similar portals. The outcomes of this study can facilitate usersā navigation, and provide a new method for building a recommendation system that recommends items at three levels: the topic level, the article level, and the Q&A item level
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Personalization via collaboration in web retrieval systems: a context based approach
World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization
Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision
Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark
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