Recommending Curated Content Using Implicit Feedback

Main Article Content

Debashish Roy


Matrix factorization (MF) which is a Collaborative filtering (CF) based model, is widely used in the recommendation systems (RS). For our experiment, we collected data from a company's internal web site where curated contents are published and pushed to the employees. However, the size of the dataset is small and interaction data is also limited. We got a sparse matrix when we generated a user-item rating matrix. We have used Multi-Layer Perceptron (MLP) to calculate the rating scores from the implicit feedbacks. However, on this sparse dataset traditional content only or CF-only RSs do not work well. Here, we propose ahybrid RS that incorporates content similarity scores into an MLP-based MF-model. To integrate the content similarity scores into the MF, we have defined an objective function based on a regularization term. The experimental result shows that our proposed model demonstrates a better result than the traditional MF-based models.

Matrix factorization, LDA, TF-IDF, collaborative filtering, regularization, objective function, NLP.

Article Details

How to Cite
Roy, D. (2020). Recommending Curated Content Using Implicit Feedback. Asian Journal of Research in Computer Science, 5(2), 10-16.
Short Research Article


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