Comparative study of recommender system approaches and movie recommendation using collaborative filtering
| Autor: | V. Uma, Taushif Anwar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mean squared error
Computer science Strategy and Management Word error rate 020207 software engineering Context (language use) 02 engineering and technology Recommender system computer.software_genre MovieLens Singular value decomposition 0202 electrical engineering electronic engineering information engineering Collaborative filtering Domain knowledge 020201 artificial intelligence & image processing Data mining Safety Risk Reliability and Quality computer |
| Zdroj: | International Journal of System Assurance Engineering and Management. 12:426-436 |
| ISSN: | 0976-4348 0975-6809 |
| DOI: | 10.1007/s13198-021-01087-x |
| Popis: | The increasing demand for personalized information has resulted in the development of the Recommender System (RS). RS has been widely utilized and broadly studied to suggest the interests of users and make an appropriate recommendation. This paper gives an overview of several types of recommendation approaches based on user preferences, ratings, domain knowledge, users demographic data, users context and also lists the advantages and disadvantages of each RS approach. In this paper, we also proposed the movie recommendation based on collaborative filtering and singular value decomposition plus-plus (SVD++). The proposed approach is compared with well-known machine learning approaches namely k nearest neighbor (K-NN), singular value decomposition (SVD) and Co-clustering. The proposed approach is experimentally verified using MovieLens 100 K datasets and error of the RS is measured using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The result shows that the proposed approach gives a lesser error rate with RMSE (0.9201) and MAE (0.7219). This approach also overcomes cold-start, data sparsity problems and provides them relevant items and services. |
| Databáze: | OpenAIRE |
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