A Study of Snippet Length and Informativeness: Behaviour, Performance and User Experience

The design and presentation of a Search Engine Results Page (SERP) has been subject to much research. With many contemporary aspects of the SERP now under scrutiny, work still remains in investigating more traditional SERP components, such as the result summary. Prior studies have examined a variety of different aspects of result summaries, but in this paper we investigate the influence of result summary length on search behaviour, performance and user experience. To this end, we designed and conducted a within-subjects experiment using the TREC AQUAINT news collection with 53 participants. Using Kullback-Leibler distance as a measure of information gain, we examined result summaries of different lengths and selected four conditions where the change in information gain was the greatest: (i) title only; (ii) title plus one snippet; (iii) title plus two snippets; and (iv) title plus four snippets. Findings show that participants broadly preferred longer result summaries, as they were perceived to be more informative. However, their performance in terms of correctly identifying relevant documents was similar across all four conditions. Furthermore, while the participants felt that longer summaries were more informative, empirical observations suggest otherwise; while participants were more likely to click on relevant items given longer summaries, they also were more likely to click on non-relevant items. This shows that longer is not necessarily better, though participants perceived that to be the case - and second, they reveal a positive relationship between the length and informativeness of summaries and their attractiveness (i.e. clickthrough rates). These findings show that there are tensions between perception and performance when designing result summaries that need to be taken into account

Web Acceptance and Usage Model: A Comparison between Goal-directed and Experiential Web Users

In this paper we analyse the Web acceptance and usage between goal-directed users and experiential users, incorporating intrinsic motives to improve the particular and explanatory TAM value –traditionally related to extrinsic motives-. A field study was conducted to validate measures used to operationalize model variables and to test the hypothesised network of relationships. The data analysis method used was Partial Least Squares (PLS).The empirical results provided strong support for the hypotheses, highlighting the roles of flow, ease of use and usefulness in determining the actual use of the Web among experiential and goal-directed users. In contrast with previous research that suggests that flow would be more likely to occur during experiential activities than goal-directed activities, we found clear evidence of flow for goal-directed activities. In particular the study findings indicate that flow might play a powerfulrole in determining the attitude towards usage,intention to useand, in turn,actual Web use among experiential and goal-directed users

QoE Modelling, Measurement and Prediction: A Review

In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. These devices connect to the Internet via network or telecommunications operators. Users usually have some expectations about the services provided to them by different operators. Users' expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. Thus, QoE measurement and prediction techniques may benefit users in availing personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switchover. This paper presents a review of the state-the-art research in the area of QoE modelling, measurement and prediction. In particular, we investigate and discuss the strengths and shortcomings of existing techniques. Finally, we present future research directions for developing novel QoE measurement and prediction technique

I Know Why You Went to the Clinic: Risks and Realization of HTTPS Traffic Analysis

Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website

CAHRS hrSpectrum (January - February 2004)

HRSpec04_02.pdf: 100 downloads, before Oct. 1, 2020

Hierarchical Exploration for Accelerating Contextual Bandits

Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically reduces the amount of exploration required. Intuitively, user preferences can be reasonably embedded in a coarse low-dimensional feature space that can be explored efficiently, requiring exploration in the high-dimensional space only as necessary. We introduce a bandit algorithm that explores within this coarse-to-fine spectrum, and prove performance guarantees that depend on how well the coarse space captures the user's preferences. We demonstrate substantial improvement over conventional bandit algorithms through extensive simulation as well as a live user study in the setting of personalized news recommendation.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012