Abstract
Several different approaches in recommender system have been suggested based on underlying rating history, but the majority of them suffer from the cold start problem (i.e., an inability to draw inferences to recommend items to new users. In this paper, a hybrid method has been proposed that combines personality traits using myPersonality application created by Facebook and the demographic characteristics into the traditional rating-based similarity computation. Majorly, personality information has been categorized into five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Aforesaid personality characteristics can efficiently address the new user problem as it will accurately predict the similarity between users.
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Tiwari, V., Ashpilaya, A., Vedita, P., Daripa, U., Paltani, P.P. (2020). Exploring Demographics and Personality Traits in Recommendation System to Address Cold Start Problem. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_37
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DOI: https://doi.org/10.1007/978-981-15-0936-0_37
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