这篇文章首次提出Non-Compensatory推荐的概念。所谓的Non-Compensatory，指的是不通过线性加权user和item的表达。
重新定义了用户对item的评分，主要是对不同的aspect采用不用等权重计算方法，比如强化某一个aspect，其他的aspects减去一个用户阈值（总之不是两个向量进行点乘）。
本文两个关于用户的decision很有意思：
（1）用户对待一个item，并不是说把所有的aspect平均下来，分数最高就喜欢，可能更多的情况是：只要有一个aspect不符合用户的预期，那么即使总分很高，用户也不会买。
（2）用户对所有的aspect的重视程度是不一样的，一般可以从高到低有一个排序。
（3）所以总结下来：用户买item的决策过程：最关心的apsect很突出，其他aspect不低于预期。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Non-Compensatory Psychological Models for Recommender Systems | |||||||||||
| 文献作者 | Chen Lin | ||||||||||
| 文献发表年限 | 2019 | ||||||||||
| 文献关键字 | |||||||||||
| Bradley-Terry Model; 偏好结构；decision procesison； 非线性加权 | |||||||||||
| 摘要描述 | |||||||||||
| The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i.e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models. Our general assumptions are (1) there are K universal hid- den aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models. | |||||||||||
