Recommending Curated Content Using Implicit Feedback

Main Article Content

Debashish Roy

Abstract

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.

Keywords:
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. https://doi.org/10.9734/ajrcos/2020/v5i230130
Section
Short Research Article

References

Aggarwal CC. Recommender systems: The textbook (1st Ed.). Springer Publishing Company, Incorporated; 2016.

Isinkaye FO, Folajimi YO, Ojokoh BA. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal. 2015;16(3):261-273.

Burke R. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction. Springer. 2002;12(4):331-370.

Lops P, De Gemmis M, Semeraro G. Recommender systems handbook. Springer. 2011;1-186.

Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of Machine Learning Research. 2003;993-1022.

Zhao G, Liu Y, Zhang W, Wang Y. TFIDF based feature words extraction and Topic Modeling for Short Text. ICMSS. Wuhan, China; 2018.

Li F, Xu G, Cao L. Two-level matrix factorization for recommender systems. Neural Computing and Applications. 2016; 27(8):2267-2278.

Tang J, Gao H, Hu X. and Liu, H. Exploiting homophily effect for trust prediction. In the Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. 2013;53-62.

Yu X, Ren X, Sun Y, Sturt B, Khandelwal U, Gu Q, Norick B, Han J. Recommendation in heterogeneous information networks with implicit user feedback. In Proceedings of the 7th ACM Conference on Recommender Systems. 2013;347-350.

Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. Cambridge University Press. 2008;39:109-133.

Wei J, He J, Chen K, Zhou Y, Tang Z. Collaborative filtering and deep learning-based recommendation system for cold start items. Expert Systems with Applications. 2017;69:29-39.

Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Computer. 2009;42(8):30-37.

Cao Y, Li W, Zheng D. A hybrid recommendation approach using LDA and probabilistic matrix factorization. Cluster Computing. 2019;22(4):8811-8821.