Basics of qualitative research: Grounded theory procedures and techniques

The use of recommender systems is an emerging trend today, when user behavior information is abundant. There are many large datasets available for analysis because many businesses are interested in future user opinions. Sophisticated algorithms that predict such opinions can simplify decision-making, improve customer satisfaction, and increase sales. However, modern datasets contain millions of records, which represent only a small fraction of all possible data. Furthermore, much of the information in such sparse datasets may be considered irrelevant for making individual recommendations. As a result, there is a demand for a way to make personalized suggestions from large amounts of noisy data.
Current recommender systems are usually all-in-one applications that provide one type of recommendation. Their inflexible architectures prevent detailed examination of recommendation accuracy and its causes. We introduce a novel architecture model that supports scalable, distributed suggestions from multiple independent nodes. Our model consists of two components, the input matrix generation algorithm and multiple platform-independent combination algorithms. A dedicated input generation component provides the necessary data for combination algorithms, reduces their size, and eliminates redundant data processing. Likewise, simple combination algorithms can produce recommendations from the same input, so we can more easily distinguish between the benefits of a particular combination algorithm and the quality of the data it receives. Such flexible architecture is more conducive for a comprehensive examination of our system.
We believe that a user's future opinion may be inferred from a small amount of data, provided that this data is most relevant. We propose a novel algorithm that generates a more optimal recommender input. Unlike existing approaches, our method sorts the relevant data twice. Doing this is slower, but the quality of the resulting input is considerably better. Furthermore, the modular nature of our approach may improve its performance, especially in the cloud computing context. We implement and validate our proposed model via mathematical modeling, by appealing to statistical theories, and through extensive experiments, data analysis, and empirical studies.
Our empirical study examines the effectiveness of accuracy improvement techniques for collaborative filtering recommender systems. We evaluate our proposed architecture model on the Netflix dataset, a popular (over 130,000 solutions), large (over 100,000,000 records), and extremely sparse (1.1%) collection of movie ratings. The results show that combination algorithm tuning has little effect on recommendation accuracy. However, all algorithms produce better results when supplied with a more relevant input. Our input generation algorithm is the reason for a considerable accuracy improvement.

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A distributed, architecture-centric approach to computing accurate recommendations from very large and sparse datasets

Quasi-experimentation - design and analysis issues for field settings - cook,td, campbell,dt

A recommender system aims to provide users with personalized online product or service recommendations to handle the increasing online information overload problem and improve customer relationship management. Various recommender system techniques have been proposed since the mid-1990s, and many sorts of recommender system software have been developed recently for a variety of applications. Researchers and managers recognize that recommender systems offer great opportunities and challenges for business, government, education, and other domains, with more recent successful developments of recommender systems for real-world applications becoming apparent. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but more importantly of the practical developments in recommender systems. This paper therefore reviews up-to-date application developments of recommender systems, clusters their applications into eight main categories: e-government, e-business, e-commerce/e-shopping, e-library, e-learning, e-tourism, e-resource services and e-group activities, and summarizes the related recommendation techniques used in each category. It systematically examines the reported recommender systems through four dimensions: recommendation methods (such as CF), recommender systems software (such as BizSeeker), real-world application domains (such as e-business) and application platforms (such as mobile-based platforms). Some significant new topics are identified and listed as new directions. By providing a state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications. Research papers on various recommender system applications are summarized.The recommender systems are examined systematically through four dimensions.The recommender system applications are classified into eight categories.Related recommendation techniques in each category are identified.Several new recommendation techniques and application areas are uncovered.

Recommender system application developments

Publisher Summary This chapter presents a study of expert–novice differences between computer users. The results of expert–novice difference studies of computer users are consistent with corresponding research in other problem solving domains. The knowledge-based approach to assessing expert–novice differences offers a fruitful theoretical framework. The list of four types of knowledge offers a starting point, but future theoretical work is needed to specify the nature of what experts and novices know. The logical goal of this theoretical work is the development of precise models corresponding to expert and novice computer users. Research on expert–novice differences in computer users also suggests some tentative practical implications, all of which require additional study. First, to the degree that experts and novices differ with respect to the knowledge they possess, there is a role for training and education. To become an expert requires the acquisition of a body of knowledge, much of which can be taught. Second, this line of research offers some suggestions concerning how programming should be taught.

From Novice to expert

In interacting with the world, people form internal representations or mental models of themselves and the objects with which they interact (Norman, 1983a). According to Norman, mental models provide predictive and explanatory powers for understanding the interaction. More abstractly, Gentner and Stevens (1983) propose that mental models focus on the way people understand a specific knowledge domain. More concretely, Carroll (1984) views mental models as information that is input into cognitive structures and processes. What are mental models? Are they always formed? When formed, what are their characteristics? What are the functional consequences of having no model (if that is possible), an immature model, or a mature model? This paper intends to explore these questions.

Mental models: concepts for human-computer interaction research

The authors present a survey of recent research in adaptive interface computer software as well as a discussion of factors that require consideration in designing this software. An adaptive interface needs to include a knowledge base that encompasses four domains. These four domains are knowledge of the current user, knowledge of the interaction scheme, knowledge of the problem task, and knowledge of the underlying system. The authors review and discuss these knowledge bases along with the positive and negative aspects of adaptive interfaces. >

Adaptive human-computer interfaces: a literature survey and perspective

Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems

This experimental study investigates the effects of animated graphic colors on attention and perceived usability of users from two cultural groups, American and Thai. The experiment employs a three-way split-plot design with one between-subjects factor and two repeated-measures factors. The between-subjects factor contains two cultural groups, American and Thai. The two repeated-measures variables are a banner background color factor with six levels and a banner font color factor with two levels. Participants search for target words from text on Web pages that contain three animated banner graphics. The findings lead to the conclusion that users across cultures tend to ignore animated banner graphics when they look for specific information on highly informative Web pages. This study also suggests influences of culture on overall performance, overall retention, and overall self-reports on usability, regardless of differences in banner color combinations. Moreover, cultural differences on the self-report of attention drawing are also revealed in each banner color usage, except yellow banners with white text. This study does not aim at exploring superiority of participants between cultures. Rather, it attempts to explore some possible cultural differences in interacting with a computer interface that could facilitate cognition and perception of users from different cultural groups.

Cultural differences on attention and perceived usability: Investigating color combinations of animated graphics

Despite their popularity, usability studies concerning Tablet PCs are lacking. This study aimed at determining user satisfaction and preference aspects of Tablet PCs in comparison to laptop PCs and pen-and-paper environments. Several common computer tasks were examined in an experimental environment on 34 college student participants. User satisfaction and preferences were measured by comprehensive questionnaires. An analysis of variance was used for the empirical comparisons. Participants did not have any difficulty in reading, direct manipulation, and form filling tasks. There was a perception of a high number of errors by the participants for the writing task in Tablet PCs. Overall, participants found the general computing capabilities and portability of Tablet PCs impressive. However, the majority did not prefer Tablet PCs to laptop PCs to meet their everyday computing needs. Results can help designers improve the overall usability of the Tablet PC and help its development as a major computing medium.

https://userpages.umbc.edu/~norcio/papers/2008/Ozok-CSLTP-IJHCI/Ozok%20Laptop-IJHCI.pdf

Anthony F. Norcio

Papers

Mental models: concepts for human-computer interaction research

Adaptive human-computer interfaces: a literature survey and perspective

Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems

Cultural differences on attention and perceived usability: Investigating color combinations of animated graphics

A Comparative Study Between Tablet and Laptop PCs: User Satisfaction and Preferences