Explanations describe product recommendations in a human interpretable way in order to achieve a goal, e.g. persuade users to buy. Unlike web product search, where users have access to diverse information as to why the products might be suitable for their needs, in the voice product search domain the amount of information that can be disclosed is inherently limited. Users in general evaluate a maximum of two products and usually buy low consideration products when using the voice channel [3]. In order to enable decision making in voice product searches we propose here a framework for generating pointwise and pairwise review-based explanations that disclose further information about the products. The POINTWISE method selects a helpful sentence from the top review of the recommended product based on a BERT-based model and uses the extracted sentence to fill a response template. The PAIRWISE method first selects a diverse pair of products - in terms of their review-based representations - from the top-k ranked products for a query, then chooses a helpful review sentence for each product in the pair, and finally fills a template with the sentences. Besides further describing the product, the PAIRWISE method gives a reference point to the users and enables a comparison of the recommendations based on two diverse products for the same information need. Our crowd-sourced evaluation of explanations based on queries from a widely used e-commerce platform shows that the proposed pairwise explanations provide statistically significant improvements compared to the POINTWISE and BASELINE methods for two goals: Effectiveness, i.e. helping users to make good decisions, and Transparency, i.e. explaining how the system works. The gains of PAIRWISE over POINTWISE and BASELINE are consistent for different subsets of data based on the diversity of the selected pairs, average product price associated with the query and the query ambiguity.

Online shoppers have a lot of information at their disposal when making a purchase decision. They can look at images of the product, read reviews, make comparisons with other products, do research online, read expert reviews, and more. Voice shopping (purchasing items via a Voice assistant such as Amazon Alexa or Google Assistant) is different. Voice introduces novel challenges as the communication channel is limited in terms of the amount of information people can and are willing to absorb. Because of this, the system should choose the single most effective nugget of information to help the customer, and present the information succinctly. In this paper we report on a within-subject user study (N = 24), in which we employed three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information. Our results suggest that: (1) supporting information from customer reviews significantly improves participants perception of system effectiveness (helping them make good decisions); (2) supporting information based on search relevance signals improves user perception of system transparency (providing insight into how the system works). We discuss the implications of our findings for providing supporting information for customers shopping by Voice.