CA-NCF

Overview

• Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend.
  • People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food.
  • The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process.
• Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information.
• We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions.
• However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations.
  • The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this project, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a pre-filtering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations.
• Quantitative results show that our method outperforms the baselines by statistically significant margins.

publications: IEEE Access Journal