Book Recommender System Using Singular Value Decomposition Combined with Slope One Algorithm

Christina, -; Baizal, Z. K. A.

doi:10.1109/icoict55009.2022.9914884

Cited by 3 publications

(2 citation statements)

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“…The similarity measure used in this study is the cosine distance, to obtain a set of items that are similar to the target item. The research by Christina, and Baizal [8] also discusses the development of a model-based CF recommender system using SVD technique enhanced with the Slope One algorithm. This combination addresses the problem of data sparsity, because the model is trained with more complete data as the Slope One algorithm fills in the empty rating data.…”

Section: Introductionmentioning

confidence: 99%

“…In the various book recommender systems that have been developed [7,8], or that use SVD techniques in other domains such as skincare [10], e-commerce products [11], and music [12], the system does not provide intensive interaction between the system and the users. This limits users' flexibility in searching for books that suit their interests.…”

Section: Introductionmentioning

confidence: 99%

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Chatbot-Based Book Recommender System Using Singular Value Decomposition

Attalariq,

Baizal

2023

josh

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In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.

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Section: Introductionmentioning

confidence: 99%