کنفرانس بین المللی محاسبات نرم

صفحه اصلی / ششمین کنفرانس بین المللی محاسبات نرم

Improving the accuracy of movie recommendation using collaborative filtering

نویسندگان :

Mahsa Nooribakhsh¹ Seyed Hadi Gharibzahedi² Fernando Gonzalez Ladron de Guevara³ Marta Fernandez Diego⁴ Mahdi Mollamotalebi⁵

1- Universitat Politècnica de València 2- Islamic Azad University, Buinzahra 3- Universitat Politecnica de Valencia 4- Universitat Politecnica de Valencia 5- Islamic Azad University, Qazvin

کلمات کلیدی :

Movie recommendation system،Cold start،User emotion،Collaborative filtering،Content-based filtering،Ensemble learning

چکیده :

Movie Recommendation Systems (MRS) play a pivotal role in delivering personalized cinematic content by analyzing users’ preferences, interactions, and behavioral patterns. However, one of the persistent challenges faced by such systems is the cold start problem, which occurs due to insufficient initial user data, thereby constraining the system’s ability to produce accurate recommendations for new users. This study aims to mitigate the cold start problem in MRS by introducing a hybrid recommendation framework that incorporates emotion-to-genre translation into the recommendation process. The proposed method integrates content-based and collaborative filtering techniques with an emotion-driven mapping model to enhance personalization and predictive accuracy. Experimental evaluations demonstrate that the proposed framework achieves superior performance in terms of accuracy compared to state-of-the-art methods. In particular, the four-model ensemble configuration exhibited higher precision, recall, and F1-score than both DNN-based and ILDNet-based baselines.