Al-Bahir Journal for Engineering and Pure Sciences


The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and 25%, respectively, resulting in more accurate and relevant movie recommendations for users. The philosophy behind our approach lies in the belief that content-based methods can overcome some limitations of collaborative filtering, especially when dealing with new or niche movies with limited user ratings. By considering the specific attributes of movies and matching them to users preferences, our system can provide more tailored recommendations, enhancing user satisfaction and engagement. Overall, our content-based movie recommendation system showcases the potential of attribute-based approaches to deliver efficient and personalized recommendations. By reducing the burden on users to find suitable movies, we aim to enrich their movie-watching experience and foster their passion for cinema.


[1] Choi Sang-Min, Ko Sang-Ki, Han Yo-Sub. A movie recommendation algorithm based on genre correlations. Expert Syst Appl 2012;39(9):8079e85.

[2] Lekakos George, Caravelas Petros. A hybrid approach for movie recommendation. Multimed Tool Appl 2008;36(1): 55e70.

[3] Das Debashis, Sahoo Laxman, Datta Sujoy. A survey on recommendation system. Int J Comput Appl 2017;160:7.

[4] Zhang Jiang, et al. Personalized real-time movie recommendation system: Practical prototype and evaluation. Tsinghua Sci Technol 2019;25(2):180e91.

[5] Rajarajeswari S, et al. Movie Recommendation System. In: Emerging research in computing, information, communication and applications. Singapore: Springer; 2019. p. 329e40.

[6] Ahmed Muyeed, , Mir Tahsin Imtiaz, Khan Raiyan. Movie recommendation system using clustering and pattern recognition network. In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE; 2018.

[7] Arora Gaurav, et al. Movie recommendation system based on users’ similarity. Int J Comput Sci Mobile Comput 2014; 3(4):765e70.

[8] Subramaniyaswamy V, et al. A personalize movie recommendation system based on collaborative filtering. Int J High Perform Comput Netw 2017;10(1e2):54e63.

[9] Harper F Maxwell, Konstan Joseph A. The movie lens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 2015;5(4):1e19.

[10] Monika D. Rokade, Dr Yogesh Kumar Sharma. Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IOSR Journal of Engineering (IOSR JEN), ISSN (e): 2250- 3021, ISSN (p): 2278- 8719.

[11] Lavanya R, Singh U, Tyagi V. A Comprehensive Survey on Movie Recommendation Systems. In: 2021 International conference on artificial intelligence and smart systems (ICAIS); 2021. p. 532e6. https://doi.org/10.1109/ ICAIS50930.2021.9395759.

[12] Immaneni N, Padmanaban I, Ramasubramanian B, Sridhar R. A meta-level hybridization approach to personalized movie recommendation. In: 2017 International conference on advances in computing, Communications and Informatics (ICACCI); 2017. p. 2193e200. https://doi.org/ 10.1109/ICACCI.2017.8126171.

[13] Hossain MA, Uddin MN. A Neural Engine for Movie Recommendation System. In: 2018 4th international conference on electrical engineering and information & communication technology (iCEEiCT); 2018. p. 443e8. https:// doi.org/10.1109/CEEICT.2018.8628128.