E-Commerce Storytelling Recommendation Using Attentional Domain-Transfer Network and Adversarial Pre-Training

In e-commerce platforms, there is an emerging type of content that tells a "story" about some merchandise in the form of multimedia (text, images, video), which is named storytelling . Well told stories, like advertisements, can inspire users to purchase the related products. Thus, e-commerce service provider is keen to disseminate storytelling items to potentially interested users. We address this requirement by a cross-domain personalized recommendation approach. Because storytelling is a new type of content, its related user actions are much less, more sparse than product-related user actions, thus we propose to use product-domain user actions to assist the identification of user preferences and to make storytelling recommendations. Our method has two technical contributions. First, since the user behavior patterns are different across the storytelling domain and the product domain, we propose an attentional domain-transfer network, which effectively selects the relevant items in the two domains to characterize user preferences. Second, although storytelling is about product, between the two domains there is a large gap: product description is objective and categorical, like "keywords," but storytelling is close to human language. To bridge the domain gap, we propose a dual-domain contrastive adversarial learning method to pre-train the feature extractors for storytelling and product simultaneously. We conduct experiments on two industrial datasets, and the results demonstrate the advantage of our proposed method that consistently outperforms the state-of-the-art methods. Besides, our method can be used to recommend storytelling to products, which is a desired functionality for product providers. Our code and models are publicly available.