<![CDATA[Non-textbook educational resources refer to books designed to enrich readers’ knowledge, insights, and skills, serving as complementary materials to formal textbooks. These may include fiction, non-fiction, biographies, self-help books, and other supplementary literature. However, the broad range of available non-textbooks targeting diverse educational levels often presents challenges for students in selecting materials that are appropriate to their academic stage. This study aims to develop a content-based recommendation system capable of recommending non-textbooks based on the reader's educational level. The recommendation process employs Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity to calculate semantic similarity between user search queries and book content. To improve relevance, a filtering mechanism based on education level is introduced prior to feature extraction. Experimental results show that applying this filtering process significantly improves the recommendation's performance, yielding an average precision of 100%. In contrast, models without the filtering process achieve only 50% precision. These findings highlight the effectiveness of contextual filtering in improving the accuracy of non-textbook recommendation systems. ]]>
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