<![CDATA[Online review platforms, such as restaurant search websites and apps, have become a primary source of information for consumers when choosing restaurants. The large number of reviews available provides insights into service quality, food taste, and previous customer experiences. However, the main challenge is managing and extracting relevant information from the diverse and unstructured reviews, which can make it difficult for users to find accurate and relevant information. This study implements an information retrieval system for Indian restaurant reviews using the Vector Space Model (VSM) algorithm to address this challenge. The dataset from Kaggle, containing 10,000 Indian restaurant reviews, was processed through tokenization, stopword removal, stemming, and text normalization. The TF-IDF method was applied for term weighting, and relevance between the user's query and reviews was calculated using VSM. The evaluation results showed a precision of 70.92%, recall of 84.81%, and F1-score of 77.25%, indicating that the system can provide relevant reviews accurately and efficiently. This system could serve as a reference for developing information retrieval systems in the culinary field and other sectors that require effective customer review analysis.]]>
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