O pinion C onv: Conversational Product Search with Grounded Opinions (2023)

Abstract: 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 real-world 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 OpinionConv, 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, @xcite delineated a five-stage process that encapsulates customer decision making (see figure 1 @xcite . 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. the absence of such interactions in online environments is perceived as a deficiency in customer service especially with respect to the third stage @xcite . customers derive post-purchase satisfaction from personal exchanges, relating to others experience, and having the opportunity to ask questions @xcite . the considerable number of online product reviews available are not a substitute for everyone, since many customers lack the patience to examine many of them, leading to post-purchase dissatisfaction and product returns. conversational ai has been suggested as a solution @xcite , with the goal of mimicking the conversational strategies of sales assistants @xcite . but despite its importance, previous research on conversational product search almost entirely neglects the third stage, or rather its opinionated aspects (section 2). recent advances in large-scale conversational language models, spearheaded by openai's chat-gpt, are driving a paradigm shift in the development of conversational technologies. nonetheless, when it comes to expressing opinions pertaining to real-world events or entities, these language models lack the necessary grounding in tangible reality. for an individual to formulate an opinion on a particular subject matter, they require exposure to the subject to relate the new experience to past ones, and importantly, an emotional perception. a language model is only capable of generating what might be termed as a "statistical average" of thirdparty opinions, if they have been part of its training data. in the context of product search, such opinions would be deemed unauthentic as they are not based on real-world experiences or substantiated knowledge. this lack of authenticity poses challenges to the effective utilization of these models when (personal) opinions play an important role. ## related work three lines of research are related to ours: opinionated question answering, conversational product search, and review-based conversation generation. ## grounded product opinion generation this section introduces the opinionconv construction pipeline to generate grounded opinionated conversations for product search based on product reviews. figure 2 gives an overview of the pipeline's individual steps, grouped into preprocessing, information search dialog generation (stage 2 of the customer decision process, which we reproduce from @xcite ), and evaluation dialog generation (stage 3, our focus). ## evaluation a volunteer who is asked to pose as a customer in a laboratory user study, and who has no real intention of investing a fairly large amount of his or her own money in the purchase of a product, does not usually have the same information needs as a real customer. at the same time, we consider it unethical to confront real customers with an early prototype of a conversational sales assistant. before a practical assistant can be developed, the basic means of generating informed opinions must first be established. in our evaluation, we therefore decided to simulate full conversations between a hypothetical customer and a hypothetical sales assistant as described in the previous section. we then designed two user studies in which we specifically investigated whether human subjects consider these conversations realistic. study 1 investigates whether subjective narratives in conversational product search are considered important compared to purely factual exchanges. study 2 investiages individuals' perceptions of the quality and realism of the conversations generated by opinionconv. we conducted the two studies by recruiting volunteers living in the us or uk on prolific. 3 ## conclusion we introduce a 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. our results confirm the conversational plausibility of the generated dialogs and reveal that people tend to exchange their personal opinions while searching for a product. in future work, we envision customer-oriented assistant for buying products that assist customers in discussing the merits of products with a sales assistant, grounded in real-world reviews.