Intelligent recommender system based on unsupervised machine learning and demographic attributes

Afoudi, Yassine; Lazaar, Mohamed; Achhab, Mohammed Al

doi:10.1016/j.simpat.2020.102198

Cited by 39 publications

(24 citation statements)

References 8 publications

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“…(1) GEXP3: using graph neural network and attention mechanism to predict user behavior [16]. (2) CFIC algorithm: using recurrent neural networks and attention mechanisms to predict user behavior [17]. (3) MLRS-CCE: the predicted action class is based on the session type to which the user's action is most similar in the previous session, where the similarity between the two sessions is calculated using the cosine similarity between the session vectors [18].…”

Section: Baseline Algorithmmentioning

confidence: 99%

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An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Zhang

2021

Scientific Programming

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The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

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Section: Baseline Algorithmmentioning

confidence: 99%

“…Scientific Programming e metrics chosen in this article are widely used in sessionbased datasets [17]. Its specific description is as follows.…”

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confidence: 99%

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Zhang

2021

Scientific Programming

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“…Furthermore, we plan to explore alternative methods for rating prediction error reduction in CF datasets, in general. Finally, we will examine the extension of the proposed algorithm so that it can include additional data sources, such IoT data [80][81][82], social network-sourced information [44,83,84], and demographic features [85][86][87], targeting at further improving rating prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

2021

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In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users’ ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of ‘black sheep near neighbours’, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.

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“…The final prediction is achieved by mixing the results from the three predictors enhanced with unlabeled data. Yassine et al (2021) presented a new intelligent RS that combines collaborative filtering with the popular clustering algorithm K-means. The authors involved user demographic attributes (gender and age) to create segmented user profiles.…”

Section: Popularity Of the Methods Combinationmentioning

confidence: 99%

A hybrid user-item-based collaborative filtering model for e-commerce recommendations

Chornous¹,

Nikolskyi²,

Wyszyński³

et al. 2021

Journal of International Studies

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Intelligent recommender system based on unsupervised machine learning and demographic attributes

Cited by 39 publications

References 8 publications

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

A hybrid user-item-based collaborative filtering model for e-commerce recommendations

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