Competence-based recommender systems: a systematic literature review

Yago, Hector; Clemente, Julia; Rodríguez, Daniel

doi:10.1080/0144929x.2018.1496276

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…Since there is a monetary cost for incorrect ND predictions, it is important that the NLD model make a prediction only when the recommender is certain. Therefore, the NLD Model can choose to abstain if it is not confident [ 83 ]. This situation is analogous to a human expert saying, “I do not know,” which is often preferable to making incorrect guesses.…”

Section: Methodsmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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Section: Methodsmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…It encompasses the working memory, control and speed of processing, and visual attention [11], [33]. Researchers have identified that the cognitive processing of the human is often related to age, exercise, and experience [5], [16]. According to the wechsler adult intelligent scale (WAIS), the cognitive style could be classified into six broad types: information, comprehension, vocabulary, similarities, object assembly, and picture arrangement [34].…”

Section: Learning Attributes Related To Backgroundmentioning

confidence: 99%

“…Research in the field of dynamic learning has been carried out extensively for many years, but the main problem that continues to be investigated in the literature is related to the model that can be reused in various e-learning systems [3], [4]. The model in this context covering adaptation model, user model, and domain knowledge model [5], [6]. The main issue addressed in this paper is modelling the user, student or learner, in a dynamic learning environment according to the situation and background learning framework [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Understanding the role of individual learner in adaptive and personalized e-learning system

Muhammad

Ariatmanto

2021

Bulletin EEI

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Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.

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“…These keywords are taken from the existing surveys (Elnabarawy et al , 2020; Bilge et al , 2013). The keywords “recommendation system and recommender system” are widely used in different recommendation domain-based SLR studies (Monti et al , 2020; Al-Walidi et al , 2020; Alyari and Navimipour, 2018; Sezgin and Özkan, 2013; Yago et al , 2018; Khan et al , 2017). Using, these keywords, we hoped to find and retrieve privacy and security articles in domains of the recommendation system.…”

Section: Threats To Validity (Ttv)mentioning

confidence: 99%

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

Beg

Khan

Anjum

2021

LHT

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PurposeSimilarly, Zhu et al. (2014) and Zhang et al. (2014) stated that addressing privacy concerns with the recommendation process is necessary for the healthy development of app recommendation. Recently, Xiao et al. (2020) mentioned that a lack of effective privacy policy hinders the development of personalized recommendation services. According to the reported work, privacy protection technology methods are too limited for mobile focusing on data encryption, anonymity, disturbance, elimination of redundant data to protect the recommendation process from privacy breaches. So, this situation motivated us to conduct a systematic literature review (SLR) to provide the viewpoint of privacy and security concerns as mentioned in current state-of-the-art in the mobile app recommendation domain.Design/methodology/approachIn this work, the authors have followed Kitchenham guidelines (Kitchenham and Charters, 2007) to devise the SLR process. According to the guidelines, the SLR process has three main phases: (1) define, (2) conduct the search and (3) report the results. Furthermore, the authors used systematic mapping approach as well to ensure the whole process.FindingsBased on the selected studies, the authors proposed three main thematic taxonomies, including architectural style, security and privacy strategies, and user-usage in the mobile app recommendation domain. From the studies' synthesis viewpoint, it is observed that the majority of the research efforts have focused on the movie recommendation field, while the mainly used privacy scheme is homomorphic encryption. Finally, the authors suggested a set of future research dimensions useful for the potential researchers interested to perform the research in the mobile app recommendation domain.Originality/valueThis is an SLR article, based on existing published research, where the authors identified key issues and future directions.

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Competence-based recommender systems: a systematic literature review

Cited by 19 publications

References 32 publications

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Understanding the role of individual learner in adaptive and personalized e-learning system

Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review

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