An improvement of a Neutrality Term in an Information-neutral Recommender System
نویسندگان
چکیده
Information-neutral recommender systems aim to make recommendations whose neutrality from the specified viewpoint is guaranteed. Such systems are developed for dissolving a filter bubble problem, which is the bias or restriction that provided to people by the influence of personalization technologies. Our previously developed system was not scalable because efficient optimization techniques could not be applied. To address this problem, we developed a constraint term for enhancing the neutrality that can be analytically differentiable.
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