Authors : Dr. M. Hemalatha; N. Sowmiya; N. Ashmitha

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/2wkmz9z8

Scribd : https://tinyurl.com/y8k2s5sv

DOI : https://doi.org/10.38124/ijisrt/26feb1330

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Personalized recommendation systems have become increasingly important in supporting intelligent decisionmaking in digital platforms, particularly in domains involving frequent and repetitive purchases such as household items. Unlike traditional recommendation scenarios that rely on explicit user ratings, household consumption is largely driven by implicit behavior patterns and usage frequency, making preference prediction more challenging. This paper presents an intelligent household item recommendation system based on the DSUM algorithm, which dynamically analyzes user–item interaction data to identify similarity patterns and generate personalized recommendations. The proposed approach utilizes structured transactional data to model user behavior and adapt recommendations according to evolving consumption needs. Performance evaluation is carried out using standard recommendation metrics, including accuracy, precision, recall, and F1-score, to assess the effectiveness of the system. Experimental results indicate that the DSUM-based model produces relevant and consistent recommendations while maintaining interpretability and computational efficiency, making it suitable for small to medium-scale household recommendation applications.

Keywords : Recommendation Systems, Household Item Prediction, DSUM Algorithm, Implicit Feedback, User Similarity, Personalized Recommendations.

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