An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset

• Published in: Journal of experimental & theoretical artificial intelligence Vol. 36; no. 8; pp. 1485 - 1511
• Main Authors: Abdolmaleki, Alireza, Rezvani, Mohammad Hossein
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 16-11-2024; Taylor & Francis Ltd
• Subjects: cold start; content-based filtering; contextual information; Datasets; Filtration; genetic algorithm; Genetic algorithms; Genre; Movie recommender system; Performance evaluation; Recommender systems
• DOI: 10.1080/0952813X.2022.2153279
• EISSN: 1362-3079
• ISSN: 0952-813X

Most research on movie recommender systems has been conducted with Collaborative Filtering (CF) methods. The lack of sufficient information about users' interests in bootstrapping is one of the most critical problems of the CF method. Using a content-based filtering method can mitigate some of these problems. On the other hand, recent research has proven that utilising contextual information about the movie, such as genre, actors, and cast, can increase the efficiency of the recommender system. This paper uses a combined Genetic Algorithm (GA) and content-based filtering to find the best combination of genre, cast, and crew weights. We first convert each movie's contextual information into a descriptive vector, including cast, crew, and genre. Then, we calculate the distance between each pair of sentence vectors using two separate approaches, fully connected and metadata-based. Finally, with GA, we tune the weight of each of the contextual information of movies to maximise the recommender system's efficiency. Performance evaluation on the well-known MovieLens dataset shows that GA can improve the Precision@k criterion by producing fewer, more accurate recommendations. Weight adjustment by GA improves the F-Measure metric by approximately 58%. This, in turn, can improve Precision and Recall metrics. Also, the GA offers a higher correct recommendation rate than other methods.