  Intelligent assistants change the way people interact with computers and make
it possible for people to search for products through conversations when they
have purchase needs. During the interactions, the system could ask questions on
certain aspects of the ideal products to clarify the users' needs. For example,
previous work proposed to ask users the exact characteristics of their ideal
items before showing results. However, users may not have clear ideas about
what an ideal item looks like, especially when they have not seen any item. So
it is more feasible to facilitate the conversational search by showing example
items and asking for feedback instead. In addition, when the users provide
negative feedback for the presented items, it is easier to collect their
detailed feedback on certain properties (aspect-value pairs) of the
non-relevant items. By breaking down the item-level negative feedback to
fine-grained feedback on aspect-value pairs, more information is available to
help clarify users' intents. So in this paper, we propose a conversational
paradigm for product search driven by non-relevant items, based on which
fine-grained feedback is collected and utilized to show better results in the
next iteration. We then propose an aspect-value likelihood model to incorporate
both positive and negative feedback on fine-grained aspect-value pairs of the
non-relevant items. Experimental results show that our model is significantly
better than state-of-the-art product search baselines without using feedback
and those baselines using item-level negative feedback.
