Abstract
This paper aims at bringing recommendation to the culinary domain in recipe recommendation. Recipe recommendation possesses certain unique characteristics unlike conventional item recommendation, as a recipe provides detailed heterogeneous information about ingredients and cooking procedure. Thus, we propose to treat recipes as an aggregation of features, which are extracted from ingredients, categories, preparation directions, and nutrition facts. We then propose a content-driven matrix factorization approach to model the latent dimension of recipes, users, and features. We also propose novel bias terms to incorporate time-dependent features. The recipe dataset is available at http://mslab.csie.ntu.edu.tw/~tim/recipe.zip
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Lin, CJ., Kuo, TT., Lin, SD. (2014). A Content-Based Matrix Factorization Model for Recipe Recommendation. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_46
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DOI: https://doi.org/10.1007/978-3-319-06605-9_46
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06604-2
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