One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems
One Size Does Not Fit All: Modeling Users' Personal Curiosity in Recommender Systems
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发表时间：
2019-06
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通讯作者：
Fakhri Abbas;Xi Niu
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作者：
Fakhri Abbas;Xi Niu
当今的推荐系统被批评为推荐太明显的项目，以引起用户的兴趣。这就是为什么推荐系统研究界提倡一些“超越准确性”的评价指标，如新奇，多样性，覆盖率和偶然性，希望促进信息发现并在很长一段时间内保持用户的兴趣。在引入新视角的同时，这些评价指标大多没有考虑到个体用户的差异：思想开放的用户可能喜欢高度新颖或多样化的推荐，而保守的用户对新奇或多样性的胃口可能没有那么大。在本文中，我们开发了一个模型来近似一个人的好奇心分布在不同水平的刺激所引导的著名的冯特曲线在心理学。我们测量了一个项目的惊喜水平，以评估刺激水平，以及它是否在用户对刺激的胃口范围内。然后，我们提出了一个推荐系统框架，考虑用户的偏好和胃口刺激的好奇心是最大限度地引起。我们的框架不同于一个典型的推荐系统，因为它利用人类的好奇心，以促进系统的内在兴趣。一系列的评价实验表明，我们的框架是能够排名较高的项目，不仅收视率高，而且响应可能性高。与传统方法相比，我们的算法生成的推荐列表具有更高的激发用户好奇心的潜力。用于评估刺激（惊喜）强度的个性化因子进一步帮助推荐器实现更小（更好）的用户间相似性。
Today's recommender systems are criticized for recommending items that are too obvious to arouse users' interest. That's why the recommender systems research community has advocated some "beyond accuracy" evaluation metrics such as novelty, diversity, coverage, and serendipity with the hope of promoting information discovery and sustain users' interest over a long period of time. While bringing in new perspectives, most of these evaluation metrics have not considered individual users' difference: an open-minded user may favor highly novel or diversified recommendations whereas a conservative user's appetite for novelty or diversity may not be that large. In this paper, we developed a model to approximate an individual's curiosity distribution over different levels of stimuli guided by the well-known Wundt curve in Psychology. We measured an item's surprise level to assess the stimulation level and whether it is in the range of the user's appetite for stimulus. We then proposed a recommendation system framework that considers both user preference and appetite for stimulus where the curiosity is maximally aroused. Our framework differs from a typical recommender system in that it leverages human's curiosity to promote intrinsic interest with the system. A series of evaluation experiments have been conducted to show that our framework is able to rank higher the items with not only high ratings but also high response likelihood. The recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to traditional approaches. The personalization factor for assessing the stimulus (surprise) strength further helps the recommender achieve smaller (better) inter-user similarity.