Contextualised Browsing in a Digital Library's Living Lab

Contextualisation has proven to be effective in tailoring \linebreak search results towards the users' information need. While this is true for a basic query search, the usage of contextual session information during exploratory search especially on the level of browsing has so far been underexposed in research. In this paper, we present two approaches that contextualise browsing on the level of structured metadata in a Digital Library (DL), (1) one variant bases on document similarity and (2) one variant utilises implicit session information, such as queries and different document metadata encountered during the session of a users. We evaluate our approaches in a living lab environment using a DL in the social sciences and compare our contextualisation approaches against a non-contextualised approach. For a period of more than three months we analysed 47,444 unique retrieval sessions that contain search activities on the level of browsing. Our results show that a contextualisation of browsing significantly outperforms our baseline in terms of the position of the first clicked item in the result set. The mean rank of the first clicked document (measured as mean first relevant - MFR) was 4.52 using a non-contextualised ranking compared to 3.04 when re-ranking the result lists based on similarity to the previously viewed document. Furthermore, we observed that both contextual approaches show a noticeably higher click-through rate. A contextualisation based on document similarity leads to almost twice as many document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201

Counterfactual Estimation and Optimization of Click Metrics for Search Engines

Optimizing an interactive system against a predefined online metric is particularly challenging, when the metric is computed from user feedback such as clicks and payments. The key challenge is the counterfactual nature: in the case of Web search, any change to a component of the search engine may result in a different search result page for the same query, but we normally cannot infer reliably from search log how users would react to the new result page. Consequently, it appears impossible to accurately estimate online metrics that depend on user feedback, unless the new engine is run to serve users and compared with a baseline in an A/B test. This approach, while valid and successful, is unfortunately expensive and time-consuming. In this paper, we propose to address this problem using causal inference techniques, under the contextual-bandit framework. This approach effectively allows one to run (potentially infinitely) many A/B tests offline from search log, making it possible to estimate and optimize online metrics quickly and inexpensively. Focusing on an important component in a commercial search engine, we show how these ideas can be instantiated and applied, and obtain very promising results that suggest the wide applicability of these techniques

Investigating the contextual requirements of the Juster Scale

Researchers have employed the Juster Scale to collect purchase probability data with notable success. Reviewing the Juster Scale studies, however, has revealed that there is considerable variation in its per¬formance. Some of these variations appeared to be caused by the context in which the Juster Scale has been presented to respondents. This paper discusses three factors that influence the context of the Juster Scale and reports the results of a study that attempted to standardise its contextual requirements. The results substantiate further the Juster Scale's satisfactory performance in collecting purchase probability data

DEVELOPING AND VALIDATING A QUALITY ASSESSMENT SCALE FOR WEB PORTALS

The Web portals business model has spread rapidly over the last few years. Despite this, there have been very few scholarly findings about which services and characteristics make a Web site a portal and which dimensions determine the customers’ evaluation of the portal’s quality. Taking the example of financial portals, the authors develop a theoretical framework of the Web portal quality construct by determining the number and nature of corresponding dimensions, which are: security and trust, basic services quality, cross-buying services quality, added values, transaction support and relationship quality. To measure the six portal quality dimensions, multi item measurement scales are developed and validated.Construct Validation, Customer Retention, E-Banking, E- Loyalty, Service Quality, Web Portals

Critical Success Factors for Positive User Experience in Hotel Websites: Applying Herzberg's Two Factor Theory for User Experience Modeling

This research presents the development of a critical success factor matrix for increasing positive user experience of hotel websites based upon user ratings. Firstly, a number of critical success factors for web usability have been identified through the initial literature review. Secondly, hotel websites were surveyed in terms of critical success factors identified through the literature review. Thirdly, Herzberg's motivation theory has been applied to the user rating and the critical success factors were categorized into two areas. Finally, the critical success factor matrix has been developed using the two main sets of data.Comment: Journal articl