论文标题
实现推荐系统中人格方面的分析
Enabling the Analysis of Personality Aspects in Recommender Systems
论文作者
论文摘要
现有的推荐系统主要集中于利用用户的反馈，例如对常见项目的评分和评论以检测类似的用户。因此，当用户之间没有共同的关注项目时，它们可能会失败。我们将此问题称为数据稀疏性，而没有对常见项目的反馈（DSW-N-FCI）。基于个性的推荐系统已经取得了巨大的成功，可以根据其个性类型来识别类似的用户。但是，文献中只有少数基于个性的推荐系统可以通过填写乏味的任务来明确发现人格，或者忽略用户个人利益和知识水平的影响，这是增加建议接受的关键因素。不同的是，我们隐式地识别了用户的性格类型，而不会承担用户的负担，并将其与用户的个人兴趣及其知识水平一起纳入。现实世界数据集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。
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.
