A Course Recommender System Using Multiple Criteria Decision Making Method

Roux, François Le; Ranjeet, Elkunchwar; Ghai, Vinit; Gao, Ya; Lu, Jie

doi:10.2991/iske.2007.238

Cited by 11 publications

(10 citation statements)

References 12 publications

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“…Hopefully, it seems, to a certain extent, feasible to discover what the user was about and/or what he intended to achieve while using techniques like in Mostow (2004). This can lead to the realization of a recommender system (Resnick and Varian, 1997; Le Roux et al , 2007).…”

Section: Evaluation Resultsmentioning

confidence: 99%

ActiveTutor: towards more adaptive features in an e‐learning framework

Fournier

Sansonnet

2008

Interactive Technology and Smart Education

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PurposeThis paper aims to sketch the emerging notion of auto‐adaptive software when applied to e‐learning software.Design/methodology/approachThe study and the implementation of the auto‐adaptive architecture are based on the operational framework “ActiveTutor” that is used for teaching the topic of computer science programming in first‐grade university courses.FindingsThe paper describes an adaptive heuristic dedicated to the display of knowledge entities which is based on the dynamic balancing of four behavioral criteria for the low‐level agents (cooperation, pedagogy, audacity, and sensibility).Practical implicationsThe heuristic may be used in all multi‐agent systems where load balancing problems appear and where an auto‐adaptive behavior is important. Originality/value –The paper presents an auto‐adaptive algorithm.

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Section: Evaluation Resultsmentioning

confidence: 99%

ActiveTutor: towards more adaptive features in an e‐learning framework

Fournier

Sansonnet

2008

Interactive Technology and Smart Education

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“…Examples are Grundy (Rich 1979) and (Le Roux et al . 2007). Context-based filtering : These RSs take into account external information, such as reviews and editorial data, together with user preferences and the item information.…”

Section: The Problemmentioning

confidence: 99%

SongRecommend: From summarization to recommendation

Tata¹,

Eugenio²

2012

Nat. Lang. Eng.

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In recent years, the availability of too much information has become a fact of life for anybody connected with the Internet. The same is true for music: because of the penetration of portable devices and the availability of millions of tracks on the web, individual music collections have become unwieldy. Users need tools to help search their own song collections, and to recommend songs they may be interested in. Whereas recommendation systems have been developed for a variety of products, a music recommendation system presents special challenges, including the ability to recommend individual songs, as opposed to entire albums, even if only full album reviews are available on-line. SongRecommend, our music recommendation system, combines information extraction and generation techniques to produce summaries of reviews of individual songs from album reviews. We present a number of evaluations for SongRecommend: intrinsic evaluations of the extraction components, and of the informativeness of the summaries; and a user study of the impact of the song review summaries on users’ decision-making processes. When presented with the summary, users were able to make quicker decisions, and their choices were more varied. Whereas the smaller size of the summary has an impact on time-on-task, users do not appear to choose a specific recommendation only based on number of words. Our work demonstrates that state-of-the-art techniques in Natural Language Processing can be integrated into an effective end-to-end system.

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“…Relevant literature has shown that usually career goals, personal interests, and background are factors that in uence students/applicants choice of programmes [1][2][3][4][5]. It has also been revealed by Ibrahim et al [1] that three out of every four students/applicants were tentative or uncertain about career goals, progression, and selection at the HEI entry.…”

Section: Introductionmentioning

confidence: 99%

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

Asabere

2022

Mobile Information Systems

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Globally, the selection of tertiary programmes for higher education in a university by prospective applicants is a daunting task. Different universities offer a wide range of programmes using different education delivery modes for teaching and learning. This creates information overload in the context of tertiary programmes. To tackle the information overload problem of tertiary programmes in the context of higher education institutions (HEIs), this paper, therefore, proposes a novel recommendation model called Collaborative and Social-Personality Aware Recommendation of Programmes (CoSPARP) for tertiary programme selection. CoSPARP utilizes a hybrid filtering system that incorporates the computation of similarities relating to the CF, personality traits, and the tie strength of users (prospective applicants) to generate effective programme recommendations for a tertiary programme applicant (TPA). The proposed CoSPARP recommendation method employs the above recommendation entities to create profiles of the TPAs as a basis of profile similarity for tertiary programme recommendations. Results of benchmarking experiments showed that CoSPARP overcomes cold-start due to the proposed (innovative) hybridization process. Additionally, using a relevant real-world dataset and suitable evaluation metrics such as precision, recall, and F-measure, CoSPARP produces more favourable outcomes in comparison to other state-of-the-art methods.

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A Course Recommender System Using Multiple Criteria Decision Making Method

Cited by 11 publications

References 12 publications

ActiveTutor: towards more adaptive features in an e‐learning framework

ActiveTutor: towards more adaptive features in an e‐learning framework

SongRecommend: From summarization to recommendation

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

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