Leveraging Deep Semantics for Sparse Recommender Systems (LDS-SRS)

RS (Recommender Systems) provide personalized suggestions to the user(s) by filtering through vast amounts of similar data, including media content, e-commerce platforms, and social networks. Traditional recommendation system (RS) methods encounter significant challenges. Collaborative Filtering (CF) is hindered by the lack of sufficient user-product engagement data, while CBF (Content Based Filtering) depends extensively on feature extraction techniques in order to describe the items, which requires an understanding of both content contextual and semantic relevance of the information. To address the sparsity issue, various matrix factorization methods have been developed, often incorporating pre-processed auxiliary information. However, existing feature extraction techniques generally fail to capture both the semantic richness and topic-level insights of textual data. This paper introduces a novel hybrid recommendation system called Topic-Driven Semantic Hybridization for Sparse Recommender Systems (LDS-SRS). The model leverages the semantic features from item descriptions and incorporates topic-specific data to effectively tackle the challenges posed by data sparsity. By extracting embeddings that capture the deep semantics of textual content—such as reviews, summaries, comments, and narratives—and embedding them into Probabilistic Matrix Factorization (PMF), the framework significantly alleviates data sparsity. The LDS-SRS framework is also computationally efficient, offering low deployment time and complexity. Experimental evaluations conducted on publicly available datasets, such as AIV (Amazon Instant Video) and Movielens (1 Million & 10 Million), demonstrate the exceptional ability of the method to handle sparse user-to-item ratings, outperforming existing leading methods. The proposed system effectively addresses data sparsity by integrating embeddings that encapsulate the deep textual semantics content, including sum-maries, comment(s), and narratives, within PMF (Probabilistic Matrix Factorization). The LDS-SRS framework is also highly efficient, characterized by minimal deployment time and low computational complexity. Experimental evaluations conducted on publicly available MovieLens (1 Million and 10 Million) and AIV (Amazon Instant Video) benchmark datasets demonstrate the framework’s exceptional ability to handle sparse user-item ratings, surpassing existing advanced methods.

@article{Alkhalil2025,
  title     = {Leveraging Deep Semantics for Sparse Recommender Systems (LDS-SRS)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Adel Alkhalil},
  doi       = {10.14569/IJACSA.2025.01602123},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602123}
}