Reducing cold start problems in educational recommender systems

Kuznetsov, Stanislav; Kordík, Pavel; Rehorek, Tomas; Dvořák, Josef; Kroha, Petr

doi:10.1109/ijcnn.2016.7727600

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Many educational researchers focus on extracting information about learning progress to help students properly. Yago et al (2018) introduced an ontology network-based student model for multiple learning environments (ON-SMMILE), which is a semantic web-based model to assist teachers in educating students [19]. It is a constructive learning model in which students are highly involved in learning.…”

Section: Data Mining Methodsmentioning

confidence: 99%

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Yazdi

Mahdavi

Kheirabadi

2022

Preprint

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Virtual learning environments have become widespread in today's society to avoid time and space constraints and to share high-quality learning resources. In the process of human-computer interaction, student behaviors are recorded instantly. This article aims to design an educational recommendation system according to the individual's interests in educational resources, which is evaluated based on clicking or downloading the source and the score given to that source by the user. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of how to be aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: 1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and 2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes, with an average accuracy of 0.9978 and an error of 0.0051 offers more appropriate recommendations than similar articles.

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Section: Data Mining Methodsmentioning

confidence: 99%

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Yazdi

Mahdavi

Kheirabadi

2022

Preprint

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“…The benefits of ontology over other approaches are explained in our previous work. [3] In this paper, we extend our research by introducing an Ontology-based algorithm that works not only for the cold-start phase but also improves recommendations in transitional and regular states in combination with collaborative filtering.…”

Section: Introductionmentioning

confidence: 94%

“…and β is the long-tail biasing parameter as proposed in [4]. We introduced this algorithm in the conference paper [3].…”

Section: User-knn Algorithmmentioning

confidence: 99%

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Improving recommendation diversity and serendipity with an Ontology-based algorithm for cold start environments

Kuznetsov

Kordík

2022

Preprint

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Every real-life system has several development phases. In the beginning, it has no interactions; wecall this the cold-start problem. After a while, where user interactions occur in the system, the sys-tem gets to a state where one part of the catalogue has some interaction that could be enough touse CF algorithms. However, there’s still a larger amount of items with a small amount of inter-action, so-called cold-item. We call this state a transition state. If most of the item in the systemhas interactions, we call this a regular state. The system needs a slightly different behaviour fromthe recommendation system at each stage. At the cold-start stage, we need exploration of thecatalogue and the involvement of the maximum number of cold-items to use CF as soon as pos-sible. In the transition phase, we need diversification because there are still many items with asmall amount of interaction. In the regular phase, we want to offer new relevant items to usersto surprise them and keep them from getting into the information bubble and thus have a betterexperience using the system. Our approach uses Ontology, which can provide semantic item simi-larity if the text attributes contain the exact words but even if the text attributes look different.This paper presents a Universal Ontological-Based Algorithm (OBACS) that uses diversificationmethods such as serendipity and diversity to improve user experience in all system developmentphases. We verify our approach experimentally on the Movie Database and describe the results.

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“…According to [16], these systems basically need three components to provide recommendations: (1) context data (2) input data, namely, user information for the system to make a recommendation, and (3) an algorithm used in the recommendation process that operates jointly the context and input data to make recommendations for users. The basic idea of the whole system is not only the resulting recommendation algorithm, but mainly the creation of the entire process, from data acquisition through data processing to further use [23], which makes it possible to build user profiles including implicit and explicit information.…”

Section: Related Studiesmentioning

confidence: 99%

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

2021

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Lifelong learning enables professionals to update their skills to face challenges in their changing work environments. In view of the wide range of courses on offer, it is important for professionals to have recommendation systems that can link them to suitable courses. Based on this premise and on our previous research, this paper proposes the use of ontology to model job sectors and areas of knowledge, and to represent professional skills that can be automatically updated using the profiled data and machine learning for clustering entities. A three-stage hybrid system is proposed for the recommendation process: semantic filtering, content filtering and heuristics. The proposed system was evaluated with a set of more than 100 user profiles that were used in a previous version of the proposed recommendation system, which allowed the two systems to be compared. The proposed recommender showed 15% improvement when using ontology and clustering with DBSCAN in recall and serendipity metrics, and a six-point increase in harmonic mean over the stored data-based recommender system.

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Reducing cold start problems in educational recommender systems

Cited by 16 publications

References 15 publications

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Dynamic Educational Recommender System Based on Improved LSTM Neural Network

Improving recommendation diversity and serendipity with an Ontology-based algorithm for cold start environments

Lifelong Learning Courses Recommendation System to Improve Professional Skills Using Ontology and Machine Learning

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