Preference elicitation plays a central role in interactive recommender
systems. Most preference elicitation approaches use either item queries that
ask users to select preferred items from a slate, or attribute queries that ask
them to express their preferences for item characteristics. Unfortunately,
users often wish to describe their preferences using soft attributes for which
no ground-truth semantics is given. Leveraging concept activation vectors for
soft attribute semantics, we develop novel preference elicitation methods that
can accommodate soft attributes and bring together both item and
attribute-based preference elicitation. Our techniques query users using both
items and soft attributes to update the recommender system's belief about their
preferences to improve recommendation quality. We demonstrate the effectiveness
of our methods vis-a-vis competing approaches on both synthetic and real-world
datasets.

基于软属性，利用概念激活向量开发了一种新颖的偏好征集方法，结合了物品和属性的偏好征集，以提高推荐系统的推荐质量。

交互推荐中的软属性偏好征求

Preference Elicitation with Soft Attributes in Interactive  Recommendation

We address how to robustly interpret natural language refinements (or
critiques) in recommender systems. In particular, in human-human recommendation
settings people frequently use soft attributes to express preferences about
items, including concepts like the originality of a movie plot, the noisiness
of a venue, or the complexity of a recipe. While binary tagging is extensively
studied in the context of recommender systems, soft attributes often involve
subjective and contextual aspects, which cannot be captured reliably in this
way, nor be represented as objective binary truth in a knowledge base. This
also adds important considerations when measuring soft attribute ranking. We
propose a more natural representation as personalized relative statements,
rather than as absolute item properties. We present novel data collection
techniques and evaluation approaches, and a new public dataset. We also propose
a set of scoring approaches, from unsupervised to weakly supervised to fully
supervised, as a step towards interpreting and acting upon soft attribute based
critiques.

讨论了如何稳定地解释推荐系统中的自然语言细化 (或批评)，提出了一种基于个性化相对语句的更自然的表述方式，并提供一组评分方法，从无监督到弱监督到全监督，在解释和处理软属性的基础上提供了方案。