Automatic Marketing Theme and Commodity Construction System for E -commerce (2023)

Abstract: When consumers’ shopping needs are concentrated, they are more interested in the collection of commodities under the specific marketing theme. Therefore, mining marketing themes and their commodities collections can help customers save shopping costs and improve user clicks and purchases for recommendation system. However, the current system invites experts to write marketing themes and select the relevant commodities, which suffer from difficulty in mass production, poor timeliness and low online indicators. Therefore, we propose a automatic marketing theme and commodity construction system, which can not only generate popular marketing themes and select the relevant commodities automatically, but also improve the theme online effectiveness in the recommendation system. Specifically, we firstly utilize the pretrained language model to generate the marketing themes. And then, we utilize the theme-commodity consistency module to select the relevant commodities for the above generative theme. What’s more, we also build the indicator simulator to evaluate the effectiveness of the above generative theme. When the indicator is lower, the above selective commodities will be input into the theme-rewriter module to generate more efficient marketing themes. Finally, we utilize the human screening to control the system quality. Both the offline experiments and online A/B test demonstrate the superior performance of our proposed system compared with state-of-the-art methods.

## introduction nowadays, e-commerce platforms have become one of the primary methods of shopping for people. customers are accustomed to browsing individual products in the traditional recommendation system, as depicted in figure 1 . however, when customers are interested in products with specific marketing themes, the current recommendation system fails to meet their shopping needs, resulting in high user browsing costs. for instance, a customer who wishes to purchase toddler shoes for their baby may be particularly interested in "warm toddler shoes in winter." they would prefer to browse warm toddler shoes from different brands and with varying prices on a single page, similar to the right image in figure 1 , rather than sifting through a mixture of individual products in the traditional recommendation system, as shown in the left image of figure 1 . therefore, it is imperative to develop a system that incorporates marketing themes and their corresponding product collections. this system would not only explore the marketing characteristics of products and present them to customers but also assist customers in quickly finding their desired products, thereby enhancing the user experience and conversion rate. in the e-commerce platform, a marketing theme is a new form of commodity that comprises a list of commodities sharing the same marketing attribute, along with a concise phrase summarizing the common attributes. as depicted in figure 1 , a primary commodity with the marketing theme (highlighted in the red box) is displayed in the recommendation flow page. if customers are attracted to the theme and the primary commodity, they can click on it and be directed to the marketing theme page, where a collection of commodities belonging to the marketing theme will be showcased. currently, the system relies on experts to manually create marketing themes and select relevant commodities based on their understanding of past experiences with commodities. they have to choose suitable commodities from a vast pool of millions and come up with an appropriate theme. however, this approach is not only costly and inefficient but also leads to low online indicators for the recommendation system. hence, there is a growing demand for e-commerce platforms to explore automatic generation methods for constructing marketing themes and commodity systems. in this study, we present an e-commerce marketing theme and commodity construction system, known as mtcc, which automatically generates marketing themes and selects relevant commodities. the task is structured as a four-step problem: marketing theme generation, selection of related commodities, simulation of indicators, and rewriting of low-quality themes. our contributions are summarized as follows: ## proposed method in this section, we provide the details of our marketing theme and commodity construction system(mtcc) for e-commerce, as shown in figure 2 . mtcc consists of four main parts: a marketing theme generation module, a related commodities selection module, an indicator simulation module, and a low-quality theme rewriting module. firstly, the pre-trained language model encodes the information of the commodities, such as their titles and attributes, to generate marketing themes that describe the marketing aspect of these commodities. to obtain attractive themes from the generation process, various strategies are employed to rank the generated themes, such as utilizing popular keywords from the e-commerce commodity search engine. secondly, a theme-commodity consistency module is utilized to select commodities that are related to the generated attractive themes. to ensure more accurate consistency, our model is further enhanced with a pretrained search engine model, which can resolve attribute conflicts, such as those related to color and size. next, an online indicator simulator, employing a pairwise loss function, is used to predict the effect of online indicators as a measure of theme quality for the generated themes. a low predicting score indicates that the generated theme may have a poor online effect and requires rewriting. finally, another pretrained language model encodes the information of the selected commodities mentioned above, generating candidate themes for the theme-commodity consistency module. this cycle continues until a high-quality marketing theme is produced. to ensure the quality of our system, we also involve manual screening of the generated themes and selected commodities. ## deployment we have successfully deployed the proposed mtcc system on a real e-commerce platform. figure 3 shows the workflow of the deployed system updated weekly. since there are thousands of commodities online, we update the model and rebuild the system every week to find novel marketing themes. in order to ensure the user experience, the marketing theme must be manually filtered before being published online. ## experiment the metric-based evaluation results are shown in ## related work to the best of our knowledge, marketing theme generation over commodity title, attribute words is a novel task in the natural language processing field. we are inspired by several closely related tasks, including headline generation @xcite @xcite @xcite @xcite and personalized content generation @xcite @xcite @xcite @xcite @xcite , which have been studied extensively in the past decades. mtcc, which automatically generates marketing themes and collects commodities under this specific marketing themes. it greatly saves users' shopping costs and improves the user click-through-rate. in order to achieve better online results, mtcc includes four modules, namely theme generation, commodity consistency, indicator simulator, and theme rewriting. extensive offline experiments and online a/b tests prove that mtcc can not only generate popular marketing themes and automatically select related items, but also improve the online effectiveness in recommender systems. in future, we can incorporate user shopping preference information into the production process to generate personalized marketing themes.