笔记:《How Much Novelty is Relevant? It Depends on Your Curiosity》
2020-03-28 18:26
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阅读日期:2020.2.12
论文题目:《How Much Novelty is Relevant? It Depends on Your Curiosity》
主要贡献:
- 提出了一个新奇的基于好奇心的推荐系统(CBRSs)框架,将推荐过程中的相关性和好奇心结合起来
- 基于心理学中发展起来的冯特曲线,建立了一个被称为概率好奇心模型(PCM)的计算模型来模拟用户的好奇心
- 提出了两种策略,考虑到推荐内容的新颖性应符合用户的好奇心水平,从而在选择推荐内容时将其趣味性和相关性结合起来
- 使用各种性能指标,包括用户间相似性、新颖性适合度和精确度,来评估CBRS的有效性。实验结果表明,CBRS不仅能够根据用户独特的好奇心提供个性化的推荐,它还提高了推荐的精度
- 研究了包含以前访问过的物品(即非新奇物品)中的建议,并表明CBRS可以正将非新奇和新奇物品进行正确的混合推荐,以优化性能。这是一个重要的发现,因为在许多应用(如音乐推荐)中,用户可能反复访问某些物品(如喜爱的歌曲)。
总结与感悟:
- 针对不同的数据集,其物品属性会对一个算法是否有效有很大影响。如本文采用的是音乐数据集,所以会有“用户听老歌”的情况,所以会考虑将非新奇物品加入到推荐候选中。可以考虑使用不同种类的数据集,去发现可能存在的、没有考虑进现有比较完备算法中去的“小漏洞”,从而进行算法的研究或改进。
- 可以借助其他领域的理论去改进算法,如本文借助了Wundt curve构造了PCM模型去模拟用户对于新奇性物品的程度。
- 可针对对某个问题的发现与改进进行思考,这个改进角度的出发点是否正确。原有的DORSs从物品属性的角度利用DU去定义好奇程度,而好奇程度应该是人的属性(应从另一个方向进行考虑,将DU用于用户好奇模型中,生成用户的好奇分数)。
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