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- W2023981330 abstract "Personalized recommender system is an indispensable application and re-shaping the world in e-commerce scopes. Following a brief review of approaches to elucidate personalized recommendation, our research work focuses on exploring a new approach of semantically associated extension by integrating the Spreading Activation model with the knowledge of chromatology to dynamically acquire the information of user preference. We attempt to apply a characteristic sequence consisted of color nodes mapping the relationships between user mood preference and item feature and illustrated the proposed approach through an instantiation of movie recommendation. This paper presents a novel insight into exploitation of rich repository of the domain-specific knowledge to elicit optimum recommendation for user." @default.
- W2023981330 created "2016-06-24" @default.
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- W2023981330 date "2011-03-01" @default.
- W2023981330 modified "2023-09-24" @default.
- W2023981330 title "Incorporating affectivity into preference elicitation for personalizesd recommendation via Spreading Activation" @default.
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- W2023981330 doi "https://doi.org/10.1109/iccrd.2011.5763910" @default.
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