Thirty years of conjoint analysis: reflections and prospects
Interfaces - Special issue: marketing engineering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Acceptance issues of personality-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
ACM Transactions on Internet Technology (TOIT)
Enhancing collaborative filtering systems with personality information
Proceedings of the fifth ACM conference on Recommender systems
How personality influences users' needs for recommendation diversity?
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Nowadays, although some approaches have been proposed to enhance the diversity in online recommendations, they neglect the user's spontaneous needs that might be possibly influenced by her/his personality. Previously, we did a user survey that showed some personality dimensions (such as conscientiousness which is one of personality factors according to the big-five factor model) have significant impact not only on users' diversity preference over items' individual attributes, but also on their overall diversity needs when all attributes are combined. Motivated by the findings, in the current work, we propose a strategy that explicitly embeds personality, as a moderating factor, to adjust the diversity degree within multiple recommendations. Moreover, we performed a user evaluation on the developed system. The experimental results demonstrate an effective solution to generate personality-based diversity in recommender systems.