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Tag-Driven Online Novel Recommendation with Collaborative Item Modeling

School of Software Engineering, South China University of Technology, Guangzhou 510006, China
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Information 2018, 9(4), 77; https://doi.org/10.3390/info9040077
Received: 10 February 2018 / Revised: 19 March 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
(This article belongs to the Special Issue AI for Digital Humanities)
Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. However, there are broad categories in the initial recommendation list achieved by collaborative filtering (CF). That is to say, it is very possible that there are many inappropriately recommended novels. Meanwhile, most algorithms assume that users can provide an explicit preference. However, this assumption does not always hold, especially in online novel reading. To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM) is proposed for online novel recommendation. Online novel reading is different from traditional book marketing and lacks preference rating. In addition, collaborative filtering frequently suffers from the Matthew effect, leading to ignored personalized recommendations and serious long tail problems. Therefore, item-based CF is improved by latent preference rating with a punishment mechanism based on novel popularity. Consequently, a tag-driven algorithm is constructed by means of collaborative item modeling and tag extension. Experimental results show that online novel recommendation is improved greatly by a tag-driven algorithm with collaborative item modeling. View Full-Text
Keywords: online novel recommendation; tag-driven; latent preference; rating prediction; item-based collaborative filtering online novel recommendation; tag-driven; latent preference; rating prediction; item-based collaborative filtering
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Li, F.; Lin, Z.; Wang, Z. Tag-Driven Online Novel Recommendation with Collaborative Item Modeling. Information 2018, 9, 77.

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