Top-N Movie Recommendations Using Machine Learning
Keywords:
Machine Learning, Movie Recommendation, N-RankingAbstract
In this paper we explore recommendation algorithms using machine learning. Specifically, our goal is to predict top-N movie recommendations using different models to give us predicted ratings for a movie. As recommendation research has shown there are several metrics to measure when evaluating top-N recommendations such as accuracy (RMSE/MAE), hit rate, coverage, and diversity. In this research, we are focusing on rating ranking and movie genre coverage. We utilize collaborative filtering, content filtering, hybrid recommenders, and finally include neural nets to generate predictions.
References or Bibliography
[Aut] No Author. Als - pyspark 3.3.0 documentation. https:
//spark.apache.org/docs/latest/api/python/reference/api/
pyspark.ml.recommendation.ALS.html.
[ea17] Xiangnan He et al. Neural collaborative filtering. in proceedings of
the 26th international conference on world wide web (www ’17). inter-
national world wide web conferences steering committee, republic and
canton of geneva, che, 173–182., 2017. https://doi.org/10.1145/
3052569.
[Fen] C. Feng. Neural collaborative filtering - machine learning notebook.
https://calvinfeng.gitbook.io/machine-learning-notebook/
supervised-learning/recommender/neural_collaborative_
filtering.
[Gar20] S. Garodia. Content-based recommender systems in
python, 2020. https://medium.com/analytics-vidhya/
content-based-recommender-systems-in-python-2b330e01eb80.
[Lan21] Fei Lang. Movie recommendation system for educational purposes
based on field-aware factorization machine, 2021. https://doi.org/
1007/s11036-021-01775-9.
[M H17] M Hendra Herviawan. Movie recommendation
based on alternating least squares (als) with apache
spark, 2017. https://hendra-herviawan.github.io/
build-movie-recommendation-with-apache-spark.html.
[sen22] seniordatascientist. Content-based recommender system with
python, 2022. https://dev.to/seniordatascientist/
content-based-recommender-system-with-python-5g85.
[Ste18] Harald Steck. Calibrated recommendations. In Proceedings of the
th ACM Conference on Recommender Systems (RecSys ’18). As-
sociation for Computing Machinery, New York, NY, USA, 154–162.,
https://doi.org/10.1145/3240323.3240372.
[Ter22] Navya Terapalli. Movie recommendations, 2022. https://github.
com/NavyaTer/Movie-Recommendations.
Downloads
Posted
License
Copyright (c) 2023 Navya Terapalli
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The copyright holder for this article has granted JSR.org a license to display the article in perpetuity.
