Approach to Cold-Start Problem in Recommender Systems in the Context of Web-Based Education

Gotardo, Reginaldo; Rafael, Estevam; Junior, Hruschka; Zorzo, Sergio Donizetti; Cereda, Paulo Roberto Massa

doi:10.1109/icmla.2013.199

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…By using the changes in CBF algorithm, we can easily show aggression at cold start problem. To permit the creation of huge recommendations we need to increase the first level skill, so that here we are using incremental semi-supervised learning to do the process [4]. To overcome the dilemma in cold start problem, recommender system with an increased rating scheme is introduced.…”

Section: The Design Beneath Displays the Fundamental Concept Back Of mentioning

confidence: 99%

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Mylavarapu¹

2018

JCC

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To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recommendation systems. The main areas which play major roles are social networking, digital marketing, online shopping and E-commerce. Recommender system consists of several techniques for recommendations. Here we used the well known approach named as Collaborative filtering (CF). There are two types of problems mainly available with collaborative filtering. They are complete cold start (CCS) problem and incomplete cold start (ICS) problem. The authors proposed three novel methods such as collaborative filtering, and artificial neural networks and at last support vector machine to resolve CCS as well ICS problems. Based on the specific deep neural network SADE we can be able to remove the characteristics of products. By using sequential active of users and product characteristics we have the capability to adapt the cold start product ratings with the applications of the state of the art CF model, time SVD++. The proposed system consists of Netflix rating dataset which is used to perform the baseline techniques for rating prediction of cold start items. The calculation of two proposed recommendation techniques is compared on ICS items, and it is proved that it will be adaptable method. The proposed method can be able to transfer the products since cold start transfers to non-cold start status. Artificial Neural Network (ANN) is employed here to extract the item content features. One of the user preferences such as temporal dynamics is used to obtain the contented characteristics into predictions to overcome those problems. For the process of classification we have used linear support vector machine classifiers to receive the better performance when compared with the earlier methods.

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Section: The Design Beneath Displays the Fundamental Concept Back Of mentioning

confidence: 99%

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Mylavarapu¹

2018

JCC

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Adaptive Starting Points in Video Learning Environments for New Learners Based on Video and Topic Tree Relations

Lehmann

2021

Educating Engineers for Future Industrial Revolutions

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Feature Selection Based Approach for Handling Cold Start Problem in Collaborative Recommender Systems

Kuanr

Mohapatra

Yesubabu³

2022

2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR)

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Approach to Cold-Start Problem in Recommender Systems in the Context of Web-Based Education

Cited by 3 publications

References 19 publications

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Adaptive Starting Points in Video Learning Environments for New Learners Based on Video and Topic Tree Relations

Feature Selection Based Approach for Handling Cold Start Problem in Collaborative Recommender Systems

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