Context-aware recommendation using rough set model and collaborative filtering

Huang, Zhengxing; Lü, Xudong; Duan, Huilong

doi:10.1007/s10462-010-9185-7

Cited by 39 publications

(30 citation statements)

References 21 publications

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“…Second, a number of mathematical notations to denote learning times of the three types of learning, lesson neighborhood and learner neighborhood are introduced in a detailed way. Third, a five-steps procedure, based on a number of hypotheses, learning times, lesson neighborhood, learner neighborhood, content filtering technique [38], collaborative filtering technique [7,8,26,28,32,37,54], similarity between two lessons and similarity between two learners, is introduced to estimate the value of EnablingTime(Lr, Ls). It denotes the amount of lesson learning-time for lesson Ls it takes to enable learner Lr to do all the exercises of Ls.…”

Section: Estimating Traditional Learning Timesmentioning

confidence: 99%

“…fourth and fifth) steps is borrowed from the content filtering [38] (resp. collaborative filtering [7,8,26,28,32,37,54] ) technique. Thus, the proposed estimation approach can benefit from the success of these two filtering techniques.…”

Section: Cumulativetime(lr Ls) This Value Is Known If Learnermentioning

confidence: 99%

“…Collaborative filtering technique [7,8,26,28,32,37,54] is the most mature and the most commonly implemented. Collaborative filtering recommends items by identifying other users with similar taste; it uses their opinion to recommend items to the active user.…”

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confidence: 99%

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A traditional-learning time predictive approach for e-learning systems in challenging environments

Belise¹

2017

EAI Endorsed Transactions on e-Learning

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The explosion of world-wide-web has offered people a large number of online courses, e-classes and e-schools. Such e-learning applications contain a wide variety of learning materials which can confuse the choices of learner to select. Although the area of recommender systems has made a significant progress over the last several years to address this problem, the issue remained fairly unexplored for challenging environments. This paper proposes an approach to predict traditional-learning times for recommender systems in such environments.

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Section: Estimating Traditional Learning Timesmentioning

confidence: 99%

Section: Cumulativetime(lr Ls) This Value Is Known If Learnermentioning

confidence: 99%

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A traditional-learning time predictive approach for e-learning systems in challenging environments

Belise¹

2017

EAI Endorsed Transactions on e-Learning

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“…Some mathematical models can be employed, such as Decision Tree, Bayesian Networks, Fuzzy Logic, Support Vector Machine, and K-Nearest Neighbors. In this investigation, the Rough Set Theory proposed by Pawlak [41,42] is employed for contextaware service rule generation due to its strength in handling uncertainty and imperfection of context data [43].…”

Section: Rst-based Rule Generation To Supervise the Service Customizamentioning

confidence: 99%

A Rule-based Service Customization Strategy for Smart Home Context-Aware Automation

Meng

2016

IEEE Trans. on Mobile Comput.

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Abstract-The continuous technical progress of the smartphone built-in modules and embedded sensing techniques has created chances for context-aware automation and decision support in home environments. Studies in this area mainly focus on feasibility demonstrations of the emerging techniques and system architecture design that are applicable to the different use cases. It lacks service customization strategies tailoring the computing service to proactively satisfy users' expectations. This investigation aims to chart the challenges to take advantage of the dynamic varying context information, and provide solutions to customize the computing service to the contextual situations. This work presents a rule-based service customization strategy which employs a semantic distance-based rule matching method for context-aware service decision making and a Rough Set Theory-based rule generation method to supervise the service customization. The simulation study reveals the trend of the algorithms in time complexity with the number of rules and context items. A prototype smart home system is implemented based on smartphones and commercially available low-cost sensors and embedded electronics. Results demonstrate the feasibility of the proposed strategy in handling the heterogeneous context for decision making and dealing with history context to discover the underlying rules. It shows great potential in employing the proposed strategy for context-aware automation and decision support in smart home applications.

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“…Baltrunas et al [6] conducted a survey asking participants to evaluate if a particular contextual factor influenced their ratings or not, in order to acquire contextual relevance from subjective judgements and further build predictive models for mobile recommender systems. Another attempt to explore contextual relevance is proposed by Huang et al [12], where they combine attributes of contexts with items directly and extract a set of significant contextual attributes to index a particular item based on rough set theory reduction. The process is similar to contextual feature selection [16] or attribute reduction, and it requires large datasets with contextual ratings under multi-dimensional contexts for training purposes.…”

Section: Related Workmentioning

confidence: 99%

Differential Context Relaxation for Context-Aware Travel Recommendation

Zheng

Burke

Mobasher

2012

Lecture Notes in Business Information Processing

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Abstract. Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of contextidentifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.

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Context-aware recommendation using rough set model and collaborative filtering

Cited by 39 publications

References 21 publications

A traditional-learning time predictive approach for e-learning systems in challenging environments

A traditional-learning time predictive approach for e-learning systems in challenging environments

A Rule-based Service Customization Strategy for Smart Home Context-Aware Automation

Differential Context Relaxation for Context-Aware Travel Recommendation

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