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- W4307363532 abstract "AbstractRecommendation systems are used to make suggestions for products to buy or view. They steer consumers to the things that will meet their demands by sifting through a big database of information. For recommending items, several techniques such as content filtering, collaborative filtering, and association mining techniques have been employed. This improves performance by integrating collaborative-based filtering with association rule mining to overcome the problem of data sparsity. The results show that the suggested recommendation algorithms outperform the competition and address issues like data sparsity and scalability. To overcome the issue of no similarity, the proposed recommending system is based on similar items by computing similarity between user chosen items and available database items.KeywordsRecommendation systemsCollaborative filteringAssociation mining" @default.
- W4307363532 created "2022-10-31" @default.
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- W4307363532 date "2022-10-28" @default.
- W4307363532 modified "2023-09-25" @default.
- W4307363532 title "Web Book Recommendation System Based on Collaborative Filtering and Association Mining" @default.
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- W4307363532 doi "https://doi.org/10.1007/978-981-19-4990-6_38" @default.
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