Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Blanco‐Fernández, Yolanda; López‐Nores, Martín; Gil‐Solla, Alberto; Ramos‐Cabrer, Manuel; Pazos‐Arias, José J.

doi:10.1016/j.ins.2011.06.016

Cited by 53 publications

(23 citation statements)

References 29 publications

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“…Such systems efficiently deal with any big length of data, evaluate the learner's interests, and automatically generate relevant materialistic learning content suggestions or recommendations [19]. The emerging term of recommender systems in elearning is known as e-learning recommender systems [3].…”

Section: Literature Backgroundmentioning

confidence: 99%

Retracted: E-Learning Recommender Systems Based on Goal-Based Hybrid Filtering

Chughtai

Selamat

Ghani

et al. 2014

International Journal of Distributed Sensor Networks

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This research work is based on the thesis contribution by proposing the goal-based hybrid filtering approach in e-learning recommender systems (eLearningRecSys). The proposed work has been used to analyze the personalized similarities between learner's profile preferences collaboratively. The proposed work consists of two hybridizations: the first hybridization has been made with content-based filtering and collaborative features to overcome the new-learners zero-rated profile recommendations issue; the second hybridization has been done with collaborative filtering and k-neighborhood scheme features to improve the averagelearner's low-rated profile recommendations issue. Therefore, the proposed goal-based hybrid filtering approach that hybridized content-based filtering, collaborative filtering and k-neighborhood features simultaneously works on both types of learner's profiles recommendation issues in e-learning environments. The experiments in the proposed work are done using the famous "MovieLens" dataset, while the evaluation of experimental results has been performed with mean of precision 83.44% and mean of recall 85.22%, respectively. t-test result shows the probability difference value of 0.29 between the proposed hybrid approach and the evaluated literature work. The results demonstrate the effectiveness of the proposed hybrid recommender systems in e-learning scenarios.

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Section: Literature Backgroundmentioning

confidence: 99%

Retracted: E-Learning Recommender Systems Based on Goal-Based Hybrid Filtering

Chughtai

Selamat

Ghani

et al. 2014

International Journal of Distributed Sensor Networks

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“…Because every movie in the training set or testing set contains at least one user who give 5-stars to it, the ratings value is 100%. 4 "Sparsity" describes the sparsity problem in recommender systems. It is expressed as a percentage of the number of users who give 5-stars to movies in the training set or testing set to the one of all users in the training set or testing set.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

“…that are valuable to the user [1,2]. Content-based [3,4] or collaborative filtering(CF) [5] techniques are commonly used techniques. CF is the most popular approach used for recommender systems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy prototype classifier based on items and its application in recommender system

Mei¹,

Gong²,

Li³

2017

IJCIS

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Currently, recommender systems (RS) are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC) in which a prototype represents a social circle's preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data set-Iris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.

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“…Hybrid recommenders try to avoid such limitations by combining two or more different approaches. For example, [11] combined a content-based technique with a knowledge-based one. On the other hand, [43,55] proposed a combination of collaborative and content-based techniques.…”

Section: Recommender Systemsmentioning

confidence: 99%