Automatic Scene-based Topic Channel Construction System for E -Commerce (2022)

Abstract: Scene marketing that well demonstrates user interests within a certain scenario has proved effective for offline shopping. To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words. As manual construction of channels is time-consuming due to billions of products as well as dynamic and diverse customers’ interests, it is necessary to leverage AI techniques to automatically construct channels for certain usage scenarios and even discover novel topics. To be specific, we first frame the channel construction task as a two-step problem, i.e., scene-based topic generation and product clustering, and propose an E-commerce Scene-based Topic Channel construction system (i.e., ESTC) to achieve automated production, consisting of scene-based topic generation model for the e-commerce domain, product clustering on the basis of topic similarity, as well as quality control based on automatic model filtering and human screening. Extensive offline experiments and online A/B test validates the effectiveness of such a novel product form as well as the proposed system. In addition, we also introduce the experience of deploying the proposed system on a real-world e-commerce recommendation platform.

## introduction recently, e-commerce platforms have become an indispensable part of people's daily life. different from brick-and-mortar stores where salespersons can hold face-to-face conversations to promote products and even recommend more products related to customers' interests, most recommendation systems of e-commerce platforms, such as taobao 1 , mainly display individual products in which users might be interested @xcite , as listed in figure 1 (indicated as recommendation flow page). recently, scene marketing has become a new marketing mode for product promotion where particular application scenarios (i.e., scene) are created to demonstrate product functions and highlight features correspondingly @xcite , which is also paramount for e-commerce platforms to improve user experience during online shopping @xcite . a practical usage scenario of products can help users better understand product functions and features, and also allow the platform to exhibit more products that hit customer's specific interests, so that the user experience and click rate might be improved. however, scenes do not always help. for example, displaying all related products belonging to the same scene in the recommendation flow page might harm the user experience, since they tend to be homogeneous. to achieve scene marketing in e-commerce platforms, this work presents a novel product form, scene-based topic channel, which consists of a list of diverse products belonging to the same scenario, together with two short phrases (or sentences) as the topic title summarizing the scene. exemplified by figure 1 , one primary product of a channel and the associated scene topic title (highlighted with red box) are displayed in the recommendation flow page. if a user is interested in the primary product and clicks on it, the user is then redirected to the topic channel page where diverse products belonging to the same usage scenario are displayed. existing ways to constructing scene-based topic channel mainly rely on expert knowledge and past experience of business operators in grouping products into different functional categories with certain scene topics @xcite @xcite . however, such methods are highly expensive with low efficiency and even impractical since there are billions of products in the e-commerce platforms. therefore, in this work, we propose an e-commerce scene-based topic channel construction system (i.e., estc) to automatically construct such scene-based topic channels, where the task is framed as a two-step problem, i.e., scene-based topic generation and product clustering. one intuitive solution to obtaining scene topics is to make use of topic models @xcite @xcite or techniques from extractive summarization @xcite , which are, however, restricted to assigning topics within a predefined limited candidate set, while there are often emerging scenes in the e-commerce fields. thus, like @xcite , we propose to generate scene-based topic titles for products, which allows to create novel topics not featured in the training set. nevertheless, in practice, the limitation of labeled data for training (around 5000 instances) hinders the generation quality of the model. on the other hand, we observe that generated topic titles, describing the same scenario, might be slightly different in formulation. simply grouping products based on exact string match of generated topic titles results in channels with rare products. to address above issues, we first develop a pre-trained model in the e-commerce field to improve generation quality. then, a semantic similarity based clustering method is designed to conduct product clustering to form the channel. finally, to ensure the user experience online, we further design a quality control module to strictly filter out undesired channels, such as inconsistent topic titles, or channels with irrelevant topic-product pairs. our contributions are summarized as follows: ## proposed method the development of the proposed estc system consists of three main parts, including scene-based topic generation for each products, scene-based product clustering to aggregate products with similar topic titles, as well as the quality control module to ensure the quality of ai-generated channels. we also include a simple data augmentation module to discover weakly supervised data in order to improve the diversity of generated topic titles. ## deployment we have successfully deployed the proposed estc system on a real-world e-commerce platform. figure 3 demonstrates the workflow of the deployed system with weekly update. firstly, the data augmentation module is utilized to augment existing online channels. the augmented data is then used to train the topic generation and encoding models. since there are thousands of millions products online, we weekly update the model and re-construct the channels to discover novel scene-based topic channels. to ensure a proper user experience, human screening is necessary before publishing channels online. we also leveraged the optical character recognition (ocr) and classification techniques to extract key information about the product from product detail images. firstly, texts are extracted from the images. then, the extracted texts are ranked in descending order of their importance and relevance using the classification model. finally, highly ranked texts are selected and merged as the final ocr input of products for topic generation. in the end, we have 5,186 topic titles created by human and 82,834 topic title candidates from product side information. we further split the whole dataset into training, validation and test set with a ratio of 80%:10%:10%. the online channels are considered as ground-truth. details are listed in ## lessons learned during deployment several lessons we have learned during model deployment could be beneficial for other like-minded practitioners who wish to deploy cutting-edge ai technologies into real-world applications, such as the importance of real-world data quality and business understandings. ## related work previous studies @xcite @xcite on topic mining mainly first retrieve candidate topic labels from reference corpora and then conduct topic ranking to select the best topic label. @xcite simply take a word from a top-n terms as the topic label. knowledge bases are also adopted to retrieve topic labels by matching candidate topic words to knowledge concepts @xcite . techniques from extractive summarization have also been used for topic extraction @xcite , which typically extract summary sentences from the input text related to topics. recent years have witnessed neural networks are successfully leveraged to improve performance of topic modeling techniques, such as incorporating neural embeddings into existing lda-like models @xcite , as well as the clustering embedding based approaches @xcite @xcite natural language processing techniques have been widely used in e-commerce fields to improve user experience, including automatic product copywriting generation @xcite , online product review generation @xcite and question generation @xcite . differently, we propose to leverage natural language generation and clustering techniques to automatically construct scene-based topic channels, which, to the best of our knowledge, is novel. ## conclusion this work aims to automatically construct scenebased topic channels. according to the understanding of business requirements, we propose to first generate topic titles for each product following by conducting product clustering to form a channel and design a novel framework, consisting of topic generation, product clustering and post-processing modules. the extensive offline experiments and online a/b test have demonstrated the effectiveness of the proposed approach. incorporating user behaviors (e.g., click preference) into channel construction processes is worthy investigating in future. for example, the generated title and the clustered product are personalized.