OpinionConv: Conversational Product Search with Grounded Opinions

When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also true in sales conversations, where a customer and a sales assistant exchange facts and opinions about products. However, training an AI for such conversations is complicated by the fact that language models do not possess authentic opinions for their lack of realworld experience. We address this problem by leveraging product reviews as a rich source of product opinions to ground conversational AI in true subjective narratives. With Opinion- Conv, we develop the first conversational AI for simulating sales conversations. To validate the generated conversations, we conduct several user studies showing that the generated opinions are perceived as realistic. Our assessors also confirm the importance of opinions as an informative basis for decision making.

Introduction. In order to elucidate the mechanics of conversational product search, Kotler and Keller (2015) delineated a five-stage process that encapsulates customer decision making (see Figure 1, left). This process suggests that the customer: (1) recognizes a problem or need; (2) searches for information about potential products or services that could resolve the problem or fulfill the need, filtering them until a manageable set of alternatives remains; (3) evaluates and compares these alternatives against each other with regard to personal preferences and third party opinions to inform their decision making; (4) proceeds to make a purchase decision predicated upon this informed evaluation; and finally, (5) exhibits post-decision behaviors that reflect their satisfaction, which completes the process. Typically, in-store shopping predominantly engages with the second and third stages of this customer decision process. Both the activities of reduc- ing the number of alternatives and evaluating their merits and demerits are conducted in conversations between customers and sales assistants.

Discussion / Conclusion. We introduce the OpinionConv, a new conversation generation pipeline that generates opinionated multi-turn conversations for product search. OpinionConv was mainly designed to incorporate subjective narratives into conversational product search. The pipeline presented in this work can be easily extended to different domains. Recent progress in conversational systems, such as Chat- GPT and YouChat, have shown tremendous improvements in natural language dialog between humans and conversational agents. However, when it comes to holding an opinionated conversation, specifically in product search, they are still limited for lack of grounding in real-world experience about products. This motivated the design of a pipeline to control both the dialog coherence and the information to be mentioned in the utterances. However, it should be mentioned that the trade-off between a coherent conversation and a more diverse conversation needs to be further studied. In order to validate the quality of the conversations generated by OpinionConv, we conduct two extensive human evaluations.