Contextualizing the blogosphere: A comparison of traditional and novel user interfaces for the web

In this paper, we investigate how contextual user interfaces affect blog reading experience. Based on a review of previous research, we argue why and how contextualization may result in (H1) enhanced blog reading experiences. In an eyetracking experiment, we tested 3 different web-based user interfaces for information spaces. The StarTree interface (by Inxight) and the Focus-Metaphor interface are compared with a standard blog interface. Information tasks have been used to evaluate and compare task performance and user satisfaction between these three interfaces. We found that both contextual user interfaces clearly outperformed the traditional blog interface, both in terms of task performance as well as user satisfaction. © 2007 Laqua, S., Ogbechie, N. and Sasse, M. A

Distribution of user-perceived usefulness of four presentation styles of opinion summarization

In this study, four opinion summarization styles were compared under an experimental environment. Thirty four participants sorted thirty two cards into five usefulness categories. Every eight cards belong to one presentation style. It was found that the users spent the shortest time on cards in “not at all useful” category. The time of viewing “extremely useful” cards was also shorter than that of “somewhat useful”, “useful”, and “very useful” cards. This result can be explained with the components of the usefulness categories. Tag clouds and Aspect oriented sentiments needed less time to view. They are the major styles in “not at all useful” and “extremely useful”. Paragraph summaries and Group samples requested more time and they took at least 50% in “somewhat useful”, “useful”, and “very useful”. The findings are consistent with our previous results

The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies

There is a growing interest on how we represent and share tagging data for the purpose of collaborative tagging systems. Conventional tags, however, are not naturally suited for collaborative processes. Being free-text keywords, they are exposed to linguistic variations like case (upper vs lower), grammatical number (singular vs. plural) as well as human typing errors. Additionally, tags depend on the personal views of the world by individual users, and are not normalized for synonymy, morphology or any other mapping. The bottom line of the problem is that tags have no semantics whatsoever. Moreover, even if a user gives some semantics to a tag while using or viewing it, this meaning is not automatically shared with computers since it’s not defined in a machine-readable way. With tagging systems increasing in popularity each day, the evolution of this technology is hindered by this problem. In this paper we discuss approaches to represent tagging activities at a semantic level. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria

Explorations in tag suggestion and query expansion

The query used in a search system is only an approximation to the user’s true information need, and as a result, many factors can reduce the quality of search results. One is query ambiguity, causing searchers with different needs to issue the same query. For example, for the query java, some users may want to find java tutorial while others may want to download java software. Other factors include a vocabulary mismatch and a lack of knowledge regarding the contents of the document collection. In any case, many users benefit from assistance in forming a good query. As a result, some commercial services provide query suggestions for many queries. In this paper, we propose a Tag Suggestion System that takes advantage of tags associated with query results to expand a searcher’s query. Since not every web page is associated with existing tags, we first build an auto-tagging system which can assign multiple tags to web pages, including news, blogs, etc. The current system contains the most popular 140 tags in del.icio.us, with high precision performance. A small user study is performed to evaluate anecdotally the performance of our Tag Suggestion System, showing better quality than the query suggestion mechanisms provided by Yahoo! and Google. The result pages of expanded queries generated by the Tag Suggestion System are also significantly better than those of the Google original system

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

Many social Web sites allow users to annotate the content with descriptive metadata, such as tags, and more recently to organize content hierarchically. These types of structured metadata provide valuable evidence for learning how a community organizes knowledge. For instance, we can aggregate many personal hierarchies into a common taxonomy, also known as a folksonomy, that will aid users in visualizing and browsing social content, and also to help them in organizing their own content. However, learning from social metadata presents several challenges, since it is sparse, shallow, ambiguous, noisy, and inconsistent. We describe an approach to folksonomy learning based on relational clustering, which exploits structured metadata contained in personal hierarchies. Our approach clusters similar hierarchies using their structure and tag statistics, then incrementally weaves them into a deeper, bushier tree. We study folksonomy learning using social metadata extracted from the photo-sharing site Flickr, and demonstrate that the proposed approach addresses the challenges. Moreover, comparing to previous work, the approach produces larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD) 201

Digital Labeling and Narrative Mapping in Mobile Remote Audio Signage: Verbalization of Routes and Generation of New Verbal Route Descriptions from Existing Route Sets

Independent navigation is a great challenge for people with visual impairments. In this project, we have designed and implemented an assisted navigation solution based on the ability of visually impaired travelers to interpret and contextualize verbal route descriptions. Previous studies have validated that if a route is verbally described in sufficient and appropriate manner then VI can use their orientation and mobility skills to successfully follow the route. In this project, we do not consider the issue how the VI will interpret the route descriptions, but we aim to identify and generate new verbal route descriptions from the existing route descriptions. We discuss different algorithms that we have used for extracting the landmarks, building graphs and generation of new route descriptions from existing route info

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Tags Are Related: Measurement of Semantic Relatedness Based on Folksonomy Network

Folksonomy and tagging systems, which allow users to interactively annotate a pool of shared resources using descriptive tags, have enjoyed phenomenal success in recent years. The concepts are organized as a map in human mind, however, the tags in folksonomy, which reflect users' collaborative cognition on information, are isolated with current approach. What we do in this paper is to estimate the semantic relatedness among tags in folksonomy: whether tags are related from semantic view, rather than isolated? We introduce different algorithms to form networks of folksonomy, connecting tags by users collaborative tagging, or by resource context. Then we perform multiple measures of semantic relatedness on folksonomy networks to investigate semantic information within them. The result shows that the connections between tags have relatively strong semantic relatedness, and the relatedness decreases dramatically as the distance between tags increases. What we find in this paper could provide useful visions in designing future folksonomy-based systems, constructing semantic web in current state of the Internet, and developing natural language processing applications

Exploring blog spaces: A study of blog reading experiences using dynamic contextual displays

In this paper we report on an eye-tracking experiment conducted with 60 participants to gain an understanding of how people interact with blog environments. We compared a standard blog interface with a novel contextual blog interface, which dynamically adjusts its contextual navigation to a selected article. We measured task performance and interaction behaviour for explorative tasks and goal-oriented search tasks. We further collected subjective feedback to evaluate user preferences. We found that participants using the contextual blog interface completed search tasks 19% faster and made 80% fewer errors. Moreover, participants using the contextual blog interface interacted more with the provided information during the exploration tasks. We did not find significant differences in user preference overall between both blog interfaces. However, a more detailed analysis of our results suggests significant demographic differences for performance, behavioural and subjective measures. © 2009 The Author