Abstract
Recommender systems have become indispensable tools for numerous industries and individual who utilize e-commerce. Although recommender systems rely on the similarities between the items to be recommended, most current research projects in this area utilize traditional algorithms for similarity measurement such as cosine distance or derivatives, etc. However, the most challenging problems occur due to the difficulties of quantification for those non-numeric values that are quite intractable and cannot be solved by using regular similarity measurement algorithms. This paper proposes a novel and effective method which utilizes a taxonomy tree to measure similarities between textual attributes based on their natural characteristics. Furthermore, a 7-type-rule to cleanse the textual terms a is implemented for improving the recommender system. Finally, we evaluate our methods by implementing a recipe recommender system. The system achieves a 74.4 % overall satisfaction rate as evaluated by its users.
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Tao, L., Liu, F., Cao, J. (2016). Taxonomy Tree Based Similarity Measurement of Textual Attributes of Items for Recommender Systems. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_21
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DOI: https://doi.org/10.1007/978-3-319-48740-3_21
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