Recommender System Framework for Academic Choices: Personality Based Recommendation Engine (PBRE)

Uddin, Muhammad Fahim; Banerjee, Soumita; Lee, Jeong-Kyu

doi:10.1109/iri.2016.70

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…Personality-based RS [82], [87], [42], [68], [17], [44] F1-measure, Precision, Recall, Success ratio, Diversity, Novelty Similarly, unexpectedness and novelty are related to serendipity because serendipitous recommendations by nature are novel. After all, the user must not be aware of them (Ge et al [29], Herlocker et al [41]).…”

Section: Relationship Between Assessment Methods In Recommender Systemmentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

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Section: Relationship Between Assessment Methods In Recommender Systemmentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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“…To improve academic choice for newly enrolled students, Uddin et al [44] proposed a personality-aware recommendation model. eir proposed recommendation model makes use of predicting educational relevance for an efficient classification of talent, which uses stochastic probability distribution modeling to help the student to choose the relevant academic field.…”

Section: Personality-aware Recommender Systemsmentioning

confidence: 99%

“…eir proposed recommendation model makes use of predicting educational relevance for an efficient classification of talent, which uses stochastic probability distribution modeling to help the student to choose the relevant academic field. Similar to [44], Elahi et al [45] proposed a novel active learning (AL) approach that exploits the user's personality-using the five-factor model (FFM).…”

Section: Personality-aware Recommender Systemsmentioning

confidence: 99%

“…ey evaluated their approach in a user study by integrating it into a mobile, context-aware recommender system that provides users with recommendations for places of interest (POIs). Similar to [44,45], Hariadi and Nurjanah [46] proposed a personality-aware book recommendation system that combines the attributes and personality traits of users.…”

Section: Personality-aware Recommender Systemsmentioning

confidence: 99%

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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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“…To evaluate their proposed system, the authors used the dataset of The International Conference on Web-Based Learning (ICWL) 2012, which includes the social ties of 78 conference participants with a total time-frame of 12 hours (720 minutes). Far from that, Fahim Uddin et al [129] Proposed a personality-aware framework to improve academic choice for newly enrolled students. Their proposed framework makes use of the research field of Predicting Educational Relevance For an Efficient Classification of Talent, which uses stochastic probability distribution modeling to help the student to choose the relevant academic field.…”

Section: E Academic Content Recommendationsmentioning

confidence: 99%

A Survey on Personality-Aware Recommendation Systems

Dhelim,

Aung,

Bouras

et al. 2021

Preprint

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With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personalityaware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems.

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Recommender System Framework for Academic Choices: Personality Based Recommendation Engine (PBRE)

Cited by 9 publications

References 21 publications

Assessment Methods for Evaluation of Recommender Systems: A Survey

Assessment Methods for Evaluation of Recommender Systems: A Survey

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

A Survey on Personality-Aware Recommendation Systems

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