F olk S cope: Intention Knowledge Graph Construction for E -commerce Commonsense Discovery (2023)

Abstract: Understanding users’ intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework, to reveal the structure of humans’ minds about purchasing items. As commonsense knowledge is usually ineffable and not expressed explicitly, it is challenging to perform information extraction. Thus, we propose a new approach that leverages the generation power of large language models (LLMs) and human-in-the-loop annotation to semi-automatically construct the knowledge graph. LLMs first generate intention assertions via e-commerce specific prompts to explain shopping behaviors, where the intention can be an open reason or a predicate falling into one of 18 categories aligning with ConceptNet, e.g., IsA, MadeOf, UsedFor, etc. Then we annotate plausibility and typicality labels of sampled intentions as training data in order to populate human judgments to all automatic generations. Last, to structurize the assertions, we propose pattern mining and conceptualization to form more condensed and abstract knowledge. Extensive evaluations and study demonstrate that our constructed knowledge graph can well model e-commerce knowledge and have many potential applications.

## introduction in e-commerce platforms, understanding users' searching or purchasing intentions can benefit and motivate a lot of recommendation tasks @xcite @xcite . intentions are mental states where agents or humans commit themselves to actions. understanding others' behaviors and mental states requires rationalizing intentional actions @xcite , where we need commonsense, or, in other words, good judgements @xcite . for example, "at a birthday party, we usually need a birthday cake." meanwhile, commonsense knowledge can be factoid @xcite , which is not invariably true, and is usually ineffable and not expressed explicitly. existing intention-based studies on recommendation are either of limited numbers of intention categories @xcite or using models to implicitly model the intention memberships @xcite . thus, it is very challenging to acquire fine-grained intention knowledge in a scalable way. existing related knowledge graphs (kgs) can be categorized into two folds. first, some general situational commonsense kgs deal with everyday social situations @xcite @xcite ), but they are not directly related to massive products on e-commerce platforms and thus not generalized well on users' behavior data even for generative models, e.g., comet @xcite . second, most e-commerce kgs leverage existing kgs, such as conceptnet @xcite and freebase @xcite , to integrate them into the e-commerce catalog data @xcite @xcite @xcite . however, such integration is still based on factual knowledge, such as isa and directorof relations, and does not truly model the commonsense knowledge for purchase intentions. although some of these kgs may include information related to space, crowd, time, function, and event, they still fall short of modeling true commonsense knowledge @xcite . existing kgs constructed for e-commerce platforms can be evaluated for their factual knowledge in terms of plausibility. however, when it comes to purchasing intentions, a person's beliefs and desires @xcite are mediated by their intentions, which can be reflected by the typicality of commonsense @xcite . for example, in figure 1 , a user bought an apple watch because "apple watches can be used for telling the time" where the reason is highly plausible (but other watches can also serve similar functions), whereas a more typical reason would be "apple watches are able to track running," or "the user is simply a fan of apple products." thus, no matter what kind of factual knowledge a kg contains, if it is not directly linked to rationalization, it cannot be regarded as typical commonsense. in addition, the task of explaining a user's rating of an item has been proposed as a means of providing recommendations. to achieve this, researchers have suggested using online reviews as a natural source of explanation @xcite . however, online reviews are often noisy and diverse and may not directly reflect the user's intention behind their purchase or rating. instead, they may reflect the consequences of the purchase or the reasons behind the user's rating. existing sources of information, such as question-answering pairs, reviews, or product descriptions, do not explicitly mention the user's intentions behind their purchases, making it a challenge to extract intentional commonsense knowledge for e-commerce. as a result, constructing an intention kg for ecommerce requires sophisticated information extraction techniques and thus remains challenging. in this paper, we propose a new framework, folkscope, to acquire intention knowledge in ecommerce. instead of performing information extraction, we start from enormous user behaviors that entail sustainable intentions, such as co-buy behaviors, and leverage the generation power of large language models (llms), e.g., gpt @xcite @xcite , to generate possible intentions of the purchasing behaviors as candidates. llms have shown the capability of memorizing factual and commonsense knowledge @xcite , and "sometimes infer approximate, partial representations of the beliefs, desires, and intentions possessed by the agent that produced the context" @xcite . as open prompts in the above example can be arbitrary and loosely constrained, we also align our prompts with 18 conceptnet relations, such as isa, haspropertyof, capableof, usedfor, etc. in addition, as the generated knowledge by llms can be noisy and may not be able to reflect human's rationalization of a purchasing action, we also perform human annotation for plausibility and typicality. given generated candidates and annotations to construct the kg, we first perform pattern mining to remove irregular generations. then we train classifiers to populate the prediction scores to all generated data. finally, for each of the generated intentions, we perform conceptualization to map the key entities or concepts in the intention to more high-level concepts so that we can build a denser and more abstract kg for future generalization. an illustration of our kg is shown in figure 1 . to assess the overall quality of our kg, we randomly sample populated assertions and estimate their quality. furthermore, we demonstrate the quality and usefulness of our kg by using it in a downstream task, cf-based (collaborative filtering) recommendation. the contributions of our work can be summarized as follows. ## intrinsic evaluations in this section, we present some examples of our constructed kg and conduct comprehensive intrinsic evaluations of kg. ## conclusion in this paper, we propose a new framework, folkscope, to acquire intention commonsense knowledge for e-commerce behaviors. we develop a human-in-the-loop semi-automatic way to construct an intention kg, where the candidate assertions are automatically generated from large language models, with carefully designed prompts to align with conceptnet commonsense relations. then we annotate both plausibility and typicality scores of sampled assertions and develop models to populate them to all generated candidates. then the high-quality assertions will be further structured using pattern mining and conceptualization to form more condensed and abstractive knowledge. we conduct extensive evaluations to demonstrate the quality and usefulness of our constructed kg. in the future, we plan extend our framework to multi-domain, multi-behavior type, multilingual @xcite and temporal @xcite scenarios for empowering more e-commerce applications.