207871495
Related Research on Implicit Ratings
A s the World Wide Web continues to grow, people find it impossible to access even a small portion of the information generated in a day from Usenet news, e-mail, and Web postings. Automated filters help us to prioritize and access only the information in which we’re interested. Because opinions differ about the importance or relevance of information, people need personalized filters. Adaptive filtering and recommendation systems are gaining significance as electronic commerce grows, and will likely extend into other information activities such as electronic library usage. Such systems must, however, “know” each user’s interest level in the material being viewed in order to build accurate profiles of the user or Web page, and to use those profiles for recommendation or filtering. The common solution, explicit ratings, involves users telling the system what they think about a piece of information. It is fairly well understood and accurate but interrupts normal browsing and reading patterns. To infer user interest, implicit ratings rely on a method that is less intrusive than direct user feedback. To collect, measure, and evaluate the predictive power of implicit interest indicators, we developed a Web browser to record user actions (implicit ratings) and explicit ratings for each page visited. We conducted a user study and studied correlations between various implicit indicators and explicit interest. We also examined how implicit interest indicators impact user privacy.
1099339
Cognitive Scales and Mental Models for Inclusive Design
In keeping with a user capability and product demand approach to product assessment, this paper examines the cognitive demands placed on users when interacting with consumer products. The eventual aim is to develop a set of cognitive capability scales that could be used in the analytical evaluation of product interfaces. We explore the dimensions of cognitive capability relevant to product interaction and describe how these may be used to evaluate a given design. Planned work addresses quantitative measurement of cognitive capabilities and predictive validation of capability scales.
327923
Implicit interest indicators
Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can ``intelligently'' determine the interest of a user and use this information to make suggestions. The common solution, ``explicit ratings'', where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more ``intelligent'' method is to useimplicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating.
Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages.
Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest.
863031
Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets
Analyzing complex scientific data, e.g., graphs and images, often requires comparison of features: regions on graphs, visual aspects of images and related metadata, some features being relatively more important. The notion of similarity for comparison is typically distance between data objects which could be expressed as distance between features. We refer to distance based on each feature as a component. Weights of components representing relative importance of features could be learned using distance function learning algorithms. However, it is seldom known which components optimize learning, given criteria such as accuracy, efficiency and simplicity. This is the problem we address. We propose and theoretically compare four component selection approaches: Maximal Path Traversal, Minimal Path Traversal, Maximal Path Traversal with Pruning and Minimal Path Traversal with Pruning. Experimental evaluation is conducted using real data from Materials Science, Nanotechnology and Bioinformatics. A trademarked software tool is developed as a highlight of this work.
22693835
The new economy: an engineer's perspective
From his twin perspectives as a career-long telecommunications engineer and Chairman of one of the UK's largest electronics companies, Sir David Brown will reflect on whether and when the New Economy, seemingly so long coming, will finally arrive. He will begin by exploring how the prospect of everything being digital; everyone having broadband; and intelligence being everywhere is changing our understanding of mobility. Then he will comment on the economic effects of that changed understanding under three headings - the macroeconomy, microeconomy and socioeconomy - before suggesting the criteria we might use to decide when the New Economy has arrived.
7098291
Low correlation between random synaptic inputs impacts considerably on the output of the Hodgkin-Huxley model
Abstract The effects of positive and negative correlation, c , between ( N =100) random synaptic inputs (each firing at 100 Hz) on the mean and coefficient of variation (CV) of interspike interval (ISI) of the Hodgkin–Huxley model are examined, for different IPSP/EPSP frequency ratios, r . Mean (ISI) and CV are approximately constant (30–40 ms, and 0.75, respectively) under independence for r ∈[0,1], unlike the leaky IF small negative correlations ( c ≃−0.005) double mean ISI and increase CV by 25%.
9261429
Understanding user behaviour and its metrics
We are pleased to bring together such a great group of people to speak about various aspects of user behaviour and trends, an important topic for us all. We are sometimes guilty of assuming we know what users want. Those assumptions have been challenged in recent years by sophisticated evidence-based studies, the impact of which has been further heightened by digital systems that are responding to the new and changing needs of researchers operating on the front lines of science. To start the programme, two such scientists will provide accounts of how they respond to the information challenges they face in their respective fields. During the day we will learn about the background motivations for, and situations in which, researchers increasingly operate. The challenge is to interpret what this means in terms of our own organisations. This is a timely issue for the British Library as it revisits its science, technology and medical strategy to meet its own changing needs as well as the needs of new generations of researchers. This will require adaptations to our information and knowledge services and policies, something I suspect applies to all ICSTI members here engaged in service provision. A relevant example of this is the British Library’s recent launch of the UK PubMed Central, a US–UK collaboration with the support of research funding agencies and driven by the Wellcome Trust. We are pleased that representatives of those organisations are here today. Participants from 12 countries are among us, making this a truly international gathering. I am delighted to welcome you all here today.
39095130
Output jitter diverges to infinity, converges to zero or remains constant
14034669
Google TV search: dual-wielding search and discovery in a large-scale product
In 2006 Google designed and implemented a TV Show search system which featured a dual-navigation control combining a powerful search system with an interactive, personalize-able TV listings grid. The dual-control system allowed users to move fluidly between explicit search and general discovery, obtain detailed information without leaving the global context and construct a personal channel for easy access and recommendations. As a complete system, TV Search integrated with Google's general search page through a mini-listings onebox and also exported information out to Google calendar, Gmail and TiVo. Building this TV Search system as a large-scale product meant addressing many technological, business and fine-grained user experience details. This paper describes the design process including the early designs, the user studies and the details of the final system including the mini-guide, main page dual-control and the integrated services on the detail pages.