Data Mining Methods for Recommender Systems

Amatriain, Xavier; Jaimes, Alejandro; Oliver, Núria; Pujol, Josep M.

doi:10.1007/978-0-387-85820-3_2

Cited by 156 publications

(101 citation statements)

References 47 publications

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“…Clustering can be used to reduce the computation cost for finding the knearest neighbors, for instance in [35]. Xue et al [39] presented a typical use of clustering in recommender systems.…”

Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

“…An artificial neural network (ANN) is an assembly of inter-connected nodes and weighted links that is inspired by the architecture of the biological brain and can be used to construct model-based recommender systems [35]. Hsu et al [37] used ANN to construct a TV recommender system, using the back-propagation neural network method to train a three-layered neural network.…”

Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

“…Bayesian classifiers are popular for model-based recommender systems [35] and are often used to derive the model for CB recommender systems. When a Bayesian network is implemented in recommender systems, each node corresponds to an item, and the states correspond to each possible vote value.…”

Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,307

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: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

Section: Computational Intelligence-based Recommendation Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Recommender system application developments: A survey

Mao

et al. 2015

Decision Support Systems

1,307

546

View full text Add to dashboard Cite

show abstract

“…After a meaningful set of contextual conditions is identified, a predictive model must be built that can predict how the evaluation of an item changes as a function of the contextual factors [6]. This model is then used to select items given a target context.…”

Section: Context-aware Recommendations Are Difficult To Computementioning

confidence: 99%

Context relevance assessment and exploitation in mobile recommender systems

Baltrunas

Ludwig

Peer

et al. 2011

Pers Ubiquit Comput

228

142

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In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a contextaware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.keywords Context Á Collaborative filtering Á Recommender system Á Mobile applications Á User study 1 Introduction

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“…Since groups do not exist, unsupervised classification (clustering) is necessary. In [1], it was highlighted that the k-means clustering algorithm is the most used in recommender systems. Moreover, we previously analyzed [7] and compared [8] a different option to group the users, by using the Louvain community detection algorithm, which produces a hierarchical partitioning of the users; however, results showed that k-means is more accurate, so this approach is also used in this study.…”

Section: Prediction Of the Missing Ratings Using A Group Modelmentioning

confidence: 99%

ART: group recommendation approaches for automatically detected groups

Boratto

Carta

2015

Int. J. Mach. Learn. & Cyber.

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Group recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the previously presented limitation. This paper presents a set of group recommender systems that automatically detect groups of users by clustering them, in order to respect a constraint on the maximum number of recommendation lists that can be produced. The proposed systems have been largely evaluated on two real-world datasets and compared with hundreds of experiments and statistical tests, in order to validate the results. Moreover, we introduce a set of best practices that help in the development of group recommender systems in this context.

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Data Mining Methods for Recommender Systems

Cited by 156 publications

References 47 publications

Recommender system application developments: A survey

Recommender system application developments: A survey

Context relevance assessment and exploitation in mobile recommender systems

ART: group recommendation approaches for automatically detected groups

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