TY - GEN
T1 - Pairwise review-based explanations for voice product search
AU - Penha, Gustavo
AU - Krikon, Eyal
AU - Murdock, Vanessa
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127450762&partnerID=8YFLogxK
U2 - 10.1145/3498366.3505828
DO - 10.1145/3498366.3505828
M3 - Conference contribution
AN - SCOPUS:85127450762
T3 - CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
SP - 300
EP - 304
BT - CHIIR 2022 - Proceedings of the 2022 Conference on Human Information Interaction and Retrieval
PB - Association for Computing Machinery (ACM)
T2 - 7th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2022
Y2 - 14 March 2022 through 18 March 2022
ER -