Development of a Hybrid Recommendation System using Collaborative Filtering and Content-Based Filtering Techniques

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@article{1708607,
      author = {Sujon Sarkar},
      title = {Development of a Hybrid Recommendation System using Collaborative Filtering and Content-Based Filtering Techniques},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {2274-2284},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708607.pdf},
      abstract = {One by one, recommendation systems help users listen to millions of streams on different digital channels. This includes e-commerce sites, streaming sites, and the most famous ones, the social media channels. Now, this research dwells on the construction of a hybrid recommendation system that nicely combines collaborative filtering with content-based filtering so that the dimensions of recommendation accuracy, personalization, and scalability could be enhanced. Collaborative filtering refers to relying on user behavior and interactions such as ratings and preferences in order to find similar users or items. Unfortunately, however, it has disadvantages such as cold-start and data sparsity. Content-based filtering, on the contrary, depends solely on item features and user profiles to recommend items similar to the ones they have liked in the past. However, such a recommendation may not sound interesting or may lead to over-specialization. The fusion of both techniques into the system to yield a hybrid recommendation system aims to draw from the confines of both of them while alleviating respective disadvantages. The architecture of the system has a flexible framework, such that it can dynamically balance and weight contributions from collaborative and content-based components using a similarity metric, metadata analysis, and hybrid scoring functions. The system is trained and validated against benchmark data in terms of real-world applicability and performance measures such as precision, recall, F1-score, and user satisfaction. Comments on the experimental assessment reveal that the suggested hybrid model offers a significant improvement over traditional recommendation methods regarding the relevancy and diversity of recommendations-it exceptionally performs under new user or item situations. The model also shows resilience to changes over time in how users behave, with the result that it can produce a more personalized, rich user experience. This work contributes toward advancing methodologies on recommendation systems and makes a substantial contribution toward developing a basis of practical knowledge necessary for intelligent, data-propelled personalized services integrated into modern digital platforms.},
      keywords = {Hybrid Recommendation System, Collaborative Filtering, Content-Based Filtering, Personalization, Cold-Start Problem, Data Sparsity, User Behavior, Recommendation Accuracy, Information Overload.},
      month = {May},
  }