Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.

中文翻译：

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在推荐系统中启用个性方面的分析

现有的推荐系统主要侧重于利用用户的反馈，例如对常见项目的评分和评论来检测相似用户。因此，当用户之间没有共同感兴趣的项目时，它们可能会失败。我们将这个问题称为对公共项目没有反馈的数据稀疏性 (DSW-n-FCI)。基于个性的推荐系统在根据个性类型识别相似用户方面取得了巨大成功。然而，文献中只有少数基于个性的推荐系统要么通过填写问卷来明确发现个性，这是一项繁琐的任务，要么忽视用户个人兴趣和知识水平的影响，作为提高个性化的关键因素。建议的接受。不同的是，我们识别用户的 个性类型隐含地对用户没有负担，并将其与用户的个人兴趣和知识水平结合起来。在真实数据集上的实验结果证明了我们模型的有效性，尤其是在 DSW-n-FCI 情况下。