Abstract
Recommendation systems are powerful tools that can alleviate system overload problems by recommending the most relevant items (contents) to users. Recommendation systems allow users to find useful, interesting items from a significantly large space and also enhance the user’s browsing experience. Relevant items are determined by predicting user’s ratings on different items. Two traditional techniques used in recommendation system are Content-Based filtering and Collaborative-Filtering. Content-Based filtering uses content of the items that the user has involved in the past to discover items that the user might be interested in. On the other hands, Collaborative-Filtering determine the similarity between users and recommends items chosen by similar users
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Nguyen, Q., Nguyen, V., Tran, D., Mai, T., Quan, T. (2019). Star2vec: From Subspace Embedding to Whole-Space Embedding for Intelligent Recommendation System (Extended Abstract). In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_7
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DOI: https://doi.org/10.1007/978-3-030-34980-6_7
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