Abstract
To overcome the shortcoming of collaborative filtering algorithm based on item lacking of considering the attributes of items, an improved collaborative filtering is proposed. This paper defines a profile system composed of several tag-weight key-value pairs and implements it by means of text similarity analysis. It calculates the similarity of different items’ profile to filter unrelated items in the recommendation process. Compared with other collaborative filtering algorithms based on item content to recommend, the proposed algorithm relies less on subjective experience from the design standpoint and can be applied at the industrial field. The experimental results on the real data set of scenic spots’ ratings show that the proposed algorithm improves the performance of collaborative filtering algorithm. The problem of collaborative filtering algorithm based on item ignoring the attributes of items is effectively alleviated to some extent.
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Bian, W., Zhang, J., Li, J., Huang, L. (2018). An Improved Collaborative Filtering Algorithm and Application in Scenic Spot Recommendation. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_21
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DOI: https://doi.org/10.1007/978-981-13-2206-8_21
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