Towards Translating Objective Product Attributes Into Customer Language (2024)

Abstract: When customers search online for a product they are not familiar with, their needs are often expressed through subjective product attributes, such as ”picture quality” for a TV or ”easy to clean” for a sofa. In contrast, the product catalog in online stores includes objective attributes such as ”screen resolution” or ”material”. In this work, we aim to find a link between the objective product catalog and the subjective needs of the customers, to help customers better understand the product space using their own words. We apply correlation-based methods to the store’s product catalog and product reviews in order to find the best potential links between objective and subjective attributes; next, Large Language Models (LLMs) reduce spurious correlations by incorporating common sense and world knowledge (e.g., picture quality is indeed affected by screen resolution, and 8k is the best one). We curate a dataset for this task and show that our combined approach outperforms correlation-only and causation-only approaches.

## introduction objective catalog attributes have been part of product search for decades @xcite @xcite . objective attributes are a set of pre-specified vocabulary of catalog product attributes (e.g., "price", "size", "brand", "material"), whose values ("$9.90", "wood") are provided by the sellers. these attributes are commonly used to to split search space as facets @xcite or for product comparison @xcite . objective attributes play an important role in defining the technical aspects of the product. however, customers tend to refer to more subjective attributes (e.g., "value-for-money", "durable", "comfortable") when referring to a product in their own language. providing this translation to the user is becoming a key problem in product search and comparison @xcite , and is even more critical for products lacking reviews. specifically, we outline use cases for both directions: search engines for user searches that use subjective terms can use this translation to correctly apply the appropriate filters. alternatively, online stores can use the translation to explain overly-technical objective attributes to users using subjective terms. given an objective product catalog as well as ratings for subjective aspects of products from the catalog, one could easily compute correlations between objective and subjective attributes. however, correlations might be spurious (due to chance), or they could be attributed to confounding factors. for example, a high number of hdmi ports is highly correlated with good picture quality because newer tvs tend to have many hdmi ports. showing such spurious relations to a knowledgeable customer can lead to distrust. one could also try to identify causal links between the objective and subjective attributes using recent large language models (llms). however, while such causal links might hold true in general, they may not be applicable to the specific product catalog. the color of a shoe could potentially affect the ease of cleaning; however, for a catalog consisting exclusively of shoes made of washable materials, the impact of color on the ease of cleaning becomes marginal. in this work, we first extract subjective and objective attribute pairs that have high correlation, based on amazon customer rating for subjective aspects. we then apply llm-backed causation prediction to identify promising objective-to-subjective mappings. this approach allows us to provide links that are grounded in both world knowledge as well as the product catalog. however, often, just mapping on the attribute level is not informative enough, i.e. it's obvious that the shoe material is affecting ease of cleaning, but, which material is easiest to clean? to that end, our defined task is to both identify a causal relationship as well as the best objective attribute value. our methods show promising results compared to several baselines on a dataset curated specifically for this novel task. our contributions are as follows: subjective product attributes in recommendation systems. a common problem in natural language @xcite @xcite is known as aspect extraction is extracting these aspects and the sentiment towards these aspects, to provide a summary of these subjective attributes for each product. one example is amazon's byfeature star rating in figure 1 that provides a rating for subjective attributes that are relevant to the product. unlike objective attributes that are objectively true, there is an inherent disagreement when it comes to subjective attributes. for example, a sofa that is comfortable for one person may be less comfortable for another. in this work, we follow the holistic definition of subjective attributes in recommender systems (rss) by @xcite . in their work, they define the three different forms of subjective attributes which we further detail in section 3. in addition, they list different research challenges but refrain from solving the problem of search recommendation with subjective attributes. other solutions address the problem of subjective attributes in rs in a more implicit approach. @xcite try to measure how soft the subjective attributes (i.e., their level of subjectivity) as to try and impact the subjective attribute rating for a given product. @xcite use subjective attributes to explain why a product was recommended for a given customer based on their review. finally, @xcite devise a subjective attribute database to allow for search using subjective terms. this problem can also be formulated as a vocabulary mismatch problem @xcite . traditionally, this problem was defined as a mismatch between the user language and the document language similarly to our use case. however, exist-ing work solve it through common approaches such as query expansion, tagging and phrase docs, yet these approaches are objective in nature and refer to the same document term in a different manner (e.g., synonyms). in contrast, we infer the relation between subjective attributes and objective attributes for a specific product type, such as "easy to clean" and the "black" color for shoes. to the best of our knowledge this problem had not been previously addressed by prior work. causal inference in recommendation systems. causal inference in recommendation systems is a well-studied area @xcite @xcite . existing recommendation systems learn the correlation in the data by trying to predict customer preference, better handling biased or missing data. for example, the recommender system can offer a phone charger after buying a phone, but not vice versa. existing works utilize traditional causal inference solutions such as structural causal models @xcite or potential outcome frameworks @xcite . recent works @xcite have evaluated modern large lanugage models (llms) on several causal inference tasks and shown that on some tasks, these models are able to outperform traditional approaches by a large margin. the vast size of these models, together with the pretraining on the entire text on the web, allow these models to detect the causal relationship between objects to some extent. while these works do not imply that complex causal reasoning has spontaneously emerged in llms, they do highlight their potential for answering causal questions that are rooted in common sense. thus, in this work we use a recently released large language model as part of our solution to detect the causal effect between the subjective and objective attributes. ## problem definition an objective product attribute is a property of the product such as price, brand, size, etc. an attribute value is an instance of the property, such as 'blue' for the color property, or '32 inch' for the size property. we define a subjective attribute as any phrase or term describing the product that can be interpreted differently by two different people. for example, in the search queries "comfortable bed sheets" or "great screen quality tv", "comfortable" and "great screen quality" are subjective terms. figure 2 : the pipeline takes the objective and subjective attribute values for all the products of a product type to discover correlated mappings, find the optimal objective value, and filter using a llm to only include causal links. however, "32 inch tv" is an objective search query since "32 inch" is a factual product attribute: the tv is either 32 inch or it is not. in order to understand the different forms of subjectivity, we adopt the framework proposed by @xcite . this framework defines three distinct forms of subjectivity: we consider the following setting: assume we are given a set of products of the same product type (e.g., televisions) from a catalog, each with its objective attributes and their corresponding values. in addition, a subset of the products are rated for a set of subjective attributes. in practice, such ratings can be procured through features such as amazon's byfeature star rating (figure 1 ), or through analyzing review texts using aspect-based sentiment analysis methods. we define the following binary classification task: in the context of a product type, given a subjective attribute, an objective attribute and a value (e.g., {"picture quality", "screen resolution", "4k"}), determine whether there is a causal relation between the subjective and objective attributes, and if so, whether the value is the best option out of all the objective attribute's values. (e.g. "screen resolution directly affects picture quality for tvs and 4k is the best resolution out of {720p, 1080p, 4k}"). ## methodology our approach combines statistical correlationbased methods and large language models (llms). we leverage the strengths of llms while grounding their outputs with customers feedback from the products. our pipeline is demonstrated in figure 2 . we are given a product type (e.g., a television). then, we apply the following steps: ## experimental study our goal in this section is to assess whether our method can discover the best objective attribute value for improving a subjective attribute. ## observations below we describe two interesting phenomena we observe in the data. contextualized mapping. our framework pro-duces mappings that link objective attributes to different subjective attributes. one can see that the mapping indeed depends on the context (that is, the product type). for example, when the query is "storage box", the objective attribute "color" does not appear to have a direct impact on any of the subjective attributes, and therefore is not mapped to any of them. however, when the query is changed to "shoes", the attribute "color" is mapped to the subjective attribute "easy to clean", and for the query "measuring cup", it is mapped to "easy to read". this demonstrates the ability of our framework to create tailored mappings that are specific to the context, rather than providing a static mapping for all queries (see more examples in customer expectations in product reviews. while most inconsistencies between the llm and the correlation-based method seem to originate from the difference between the general opinion and the products available in the amazon catalog, we find that there are a few inconsistencies that originate from customer bias. for instance, our findings indicate that metal chairs are often rated as more "lightweight" than plastic chairs, despite the latter being objectively lighter. similarly, customers rate ashtrays made of crystal as sturdier than those made of metal, although metal is generally considered sturdier. we posit that these discrepancies can be explained by the fact that customers evaluate a product based on their preconceived expectations of it. therefore, when evaluating a crystal ashtray, customers may rate it as sturdy relative to their expectations of it being fragile, rather than in comparison to ashtrays made of metal. such divergent expectations can skew product ratings and make it difficult to make objective product comparisons. consequently, in those rare cases, relying solely on customer feedback without considering individual expectations can lead to erroneous conclusions and hinder accurate product comparisons. therefore, incorporating the world knowledge embedded in the llm, such as the fact that metal is heavier than plastic, is crucial to account for such biases. ## conclusions in this work, we define a novel task of mapping objective product catalog attributes to subjective product aspects. we show that combining correlation-based and causation-based methods (with state-of-the-art llms for causality) out- method query objective att. type objective att. value subjective att. corr-llm-gpt shoes color blue easy to clean measuring cup color gold easy to read ashtray material ceramic heat resistance apron material polyester blend wrinkle-free correlation-only chair material metal lightweight ashtray material crystal sturdy as future work, we outline the problem of incorporating customer expectations in product reviews. we posit that customers sometimes rate subjective aspects based on expectation from the product itself rather than the broad product category, making direct comparison between ratings inaccurate. we believe that subjectivity is an under-studied area that could benefit many ai domains involving natural language, and hope this work would spur further research on this important and complex topic.