Sparsity Reduction in Collaborative Recommendation: A Case-Based Approach

Wilson, David C.; Smyth, Barry; O'Sullivan, Derry

doi:10.1142/s0218001403002678

Cited by 35 publications

(15 citation statements)

References 6 publications

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“…According to Burke [27], there are seven basic hybridization mechanisms of combinations used in recommender systems to build hybrids: weighted [28], mixed [29], switching [30], feature combination, feature augmentation [31,32], cascade [14] and meta-level [33]. The most common practice in the existing hybrid recommendation techniques is to combine the CF recommendation techniques with the other recommendation techniques in an attempt to avoid cold-start, sparseness and/or scalability problems [3,34].…”

Section: Hybrid Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,356

546

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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 mobilebased platforms). Some significant new topics are identified and listed as new directions. By providing a stateof-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in recommender system applications.

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Section: Hybrid Recommendation Techniquesmentioning

confidence: 99%

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,356

546

View full text Add to dashboard Cite

show abstract

“…Therefore, learner can receive proper recommendations without help from other learners. Content-based techniques can be classified into two different categories (Schmitt and Bergmann 1999;Aguzzoli et al 2002;Wilson et al 2003):…”

Section: Content-based Techniquesmentioning

confidence: 99%

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

2015

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With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.

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“…[15] exploits the implicit similarity knowledge via itemitem association rule mining and case-based reasoning. [16] smooths the rating matrix by the average rating values within user clusters.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Transfer among Heterogeneous Information Networks

Xiang

Liu

Pan

et al. 2009

2009 IEEE International Conference on Data Mining Workshops

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Online recommendation systems are becoming more and more popular with the development of web. However, a critical problem of such system is that new users and items are always added to the system with time. How to overcome the data sparseness for such new incoming entities become an important issue. In this paper, we try to reduce the data sparseness in the link prediction problem via involving heterogeneous information network as auxiliary information sources. We developed two models based on the Collective Matrix Factorization (CMF) framework. We also provided a detailed empirical study on how effectively different information networks could help with two real world link prediction tasks. We will report some preliminary results of our current work and also point our several potential research issues.

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Sparsity Reduction in Collaborative Recommendation: A Case-Based Approach

Cited by 35 publications

References 6 publications

Recommender system application developments: A survey

Recommender system application developments: A survey

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

Knowledge Transfer among Heterogeneous Information Networks

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