The growing volume of data demands intelligent systems to process complex information, yet in business contexts, an excess of options can overwhelm customers. Existing recommendation systems address this through various approaches, notably Topic Modeling with Latent Dirichlet Allocation (LDA) [1] and neural network-based sequential recommendation models like Time Interval-aware Self-Attention Sequential Recommendation (TiSASRec) [2]. LDA can typically process customer reviews to generate topic-based user groups and in this way, we can recommend products within similar clusters. However, it is primarily designed for clustering rather than direct recommendation. In contrast, TiSASRec captures sequential user preferences by incorporating time intervals between interactions but often struggles with ranking relevant items. As each technique has its limitations, this research proposes a hybrid model that integrates the strengths of both approaches to improve recommendation accuracy in e-commerce transactions.