A Cognitively Inspired Clustering Approach for Critique-Based Recommenders

The purpose of recommender systems is to support humans in the purchasing decision-making process. Decision-making is a human activity based on cognitive information. In the field of recommender systems, critiquing has been widely applied as an effective approach for obtaining users' feedback on recommended products. In the last decade, there have been a large number of proposals in the field of critique-based recommenders. These proposals mainly differ in two aspects: in the source of data and in how it is mined to provide the user with recommendations. To date, no approach has mined data using an adaptive clustering algorithm to increase the recommender's performance. In this paper, we describe how we added a clustering process to a critique-based recommender, thereby adapting the recommendation process and how we defined a cognitive user preference model based on the preferences (i.e., defined by critiques) received by the user. We have developed several proposals based on clustering, whose acronyms are MCP, CUM, CUM-I, and HGR-CUM-I. We compare our proposals with two well-known state-of-the-art approaches: incremental critiquing (IC) and history-guided recommendation (HGR). The results of our experiments showed that using clustering in a critique-based recommender leads to an improvement in their recommendation efficiency, since all the proposals outperform the baseline IC algorithm. Moreover, the performance of the best proposal, HGR-CUM-I, is significantly superior to both the IC and HGR algorithms. Our results indicate that introducing clustering into the critique-based recommender is an appealing option since it enhances overall efficiency, especially with a large data set

Evaluating product search and recommender systems for E-commerce environments

Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper describes the design and implementation of two user studies in which a particular product search tool, known as example critiquing, was evaluated against a chosen baseline model. The results confirm that example critiquing significantly reduces users' task time and error rate while increasing decision accuracy. Additionally, the results of the second user study show that a particular implementation of example critiquing also made users more confident about their choices. The main contribution is that through these two user studies, an evaluation framework of three criteria was successfully identified, which can be used for evaluating general product search and recommender systems in E-commerce environments. These two experiments and the actual procedures also shed light on some of the most important issues which need to be considered for evaluating such tools, such as the preparation of materials for evaluation, user task design, the context of evaluation, the criteria, the measures and the methodology of result analyse

Data-driven decision making in Critique-based recommenders: from a critique to social media data

In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones

What would learning in an open world look like? A vision for the future

The pace of current technological advancement is phenomenal. In the last few years we have seen the emergence of ever more sophisticated gaming technologies, rich, immersive virtual worlds and new social networking services that enable learners and teachers to connect and communicate in new ways. The pace of change looks set to continue as annual Horizon reports testify (http://www.nmc.org/horizon). Clearly new technologies offer much in an educational context, with the promise of flexible, personalised and student-centred learning. Indeed research over the past few years, looking at learners' use of technologies, has given us a rich picture of how learners of all ages are appropriating new tools within their own context, mixing different applications for finding/managing information and for communicating with others (Sharpe and Beetham, forthcoming)

A general framework for intelligent recommender systems

AbstractIn this paper, we propose a general framework for an intelligent recommender system that extends the concept of a knowledge-based recommender system. The intelligent recommender system exploits knowledge, learns, discovers new information, infers preferences and criticisms, among other things. For that, the framework of an intelligent recommender system is defined by the following components: knowledge representation paradigm, learning methods, and reasoning mechanisms. Additionally, it has five knowledge models about the different aspects that we can consider during a recommendation: users, items, domain, context and criticisms. The mix of the components exploits the knowledge, updates it and infers, among other things. In this work, we implement one intelligent recommender system based on this framework, using Fuzzy Cognitive Maps (FCMs). Next, we test the performance of the intelligent recommender system with specialized criteria linked to the utilization of the knowledge in order to test the versatility and performance of the framework

Evaluating recommender systems from the user's perspective: survey of the state of the art

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

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Science in the New Zealand Curriculum e-in-science

This milestone report explores some innovative possibilities for e-in-science practice to enhance teacher capability and increase student engagement and achievement. In particular, this report gives insights into how e-learning might be harnessed to help create a future-oriented science education programme. “Innovative” practices are considered to be those that integrate (or could integrate) digital technologies in science education in ways that are not yet commonplace. “Future-oriented education” refers to the type of education that students in the “knowledge age” are going to need. While it is not yet clear exactly what this type of education might look like, it is clear that it will be different from the current system. One framework used to differentiate between these kinds of education is the evolution of education from Education 1.0 to Education 2.0 and 3.0 (Keats & Schmidt, 2007). Education 1.0, like Web 1.0, is considered to be largely a one-way process. Students “get” knowledge from their teachers or other information sources. Education 2.0, as defined by Keats and Schmidt, happens when Web 2.0 technologies are used to enhance traditional approaches to education. New interactive media, such as blogs, social bookmarking, etc. are used, but the process of education itself does not differ significantly from Education 1.0. Education 3.0, by contrast, is characterised by rich, cross-institutional, cross-cultural educational opportunities. The learners themselves play a key role as creators of knowledge artefacts, and distinctions between artefacts, people and processes become blurred, as do distinctions of space and time. Across these three “generations”, the teacher’s role changes from one of knowledge source (Education 1.0) to guide and knowledge source (Education 2.0) to orchestrator of collaborative knowledge creation (Education 3.0). The nature of the learner’s participation in the learning also changes from being largely passive to becoming increasingly active: the learner co-creates resources and opportunities and has a strong sense of ownership of his or her own education. In addition, the participation by communities outside the traditional education system increases. Building from this framework, we offer our own “framework for future-oriented science education” (see Figure 1). In this framework, we present two continua: one reflects the nature of student participation (from minimal to transformative) and the other reflects the nature of community participation (also from minimal to transformative). Both continua stretch from minimal to transformative participation. Minimal participation reflects little or no input by the student/community into the direction of the learning—what is learned, how it is learned and how what is learned will be assessed. Transformative participation, in contrast, represents education where the student or community drives the direction of the learning, including making decisions about content, learning approaches and assessment