Genre-aware user profiling using duration count matrices: A novel approach to enhancing content recommendation systems
Ali Alqazzaz, Zunaira Anwar, Mahmood ul Hassan, Shahnawaz Qureshi, Mohammad Alsulami, Ali Zia, Sultan Alyami, Syed Muhammad Zeeshan Iqbal, Sajid Anwar, Asadullah Shaikh, Ali B. Mahmoud, Ali B. Mahmoud, Ali B. Mahmoud, Ali B. Mahmoud
TL;DR
This paper introduces a new method for content recommendation systems that uses watch-time data to better understand and predict user preferences over time.
Contribution
The novel Duration Count Matrix (DCM) technique captures evolving user preferences through watch-time analysis and outperforms existing methods.
Findings
The DCM approach significantly outperformed existing methods in precision, recall, F1-score, and accuracy.
DCM-UP dynamically updates user profiles to reflect changing preferences over time.
DCM-US improves user similarity identification through collaborative filtering.
Abstract
Recommender systems play a vital role in enhancing the user experience and facilitating content discovery on online platforms. However, conventional approaches often struggle to capture users’ evolving preferences over time, leading to suboptimal performance as recommended videos frequently do not align with users’ interests. To address this issue, this study introduces an innovative method that leverages watch-time duration to analyze long-term user behavior and generate personalized recommendations. The proposed Duration Count Matrix (DCM) technique includes two key components: User Profiling (DCM-UP) and User Similarity (DCM-US). DCM-UP constructs dynamic user profiles based on engagement with content, while DCM-US quantifies user similarity through collaborative filtering, enabling the system to predict user-to-user behavior and personalize recommendations. This innovative system,…
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Taxonomy
TopicsRecommender Systems and Techniques · Digital Marketing and Social Media · Caching and Content Delivery