Recommendations usually focus on immediate accuracy metrics like Click-Through Rate (CTR), ignoring user long-term metrics. User retention, which reflects the percentage of today’s users who will return to the system in the next few days, should be paid more attention to. However, most existing methods did not focus on user retention, since their complexity and uncertainty make it extremely hard to discover why a user will or will not return to a system. Recently, a few pioneers have optimized user retention, focusing solely on accuracy without delving into its underlying rationale. This is primarily due to the absence of explicit supervised signals. In this work, we design a Behavior-wise Contrastive Multi-Instance Learning (BCMIL) module, which jointly models clicked and impressed items to capture interpretable user retention. Specifically, we conduct in-depth analyses in real-world scenarios to discover implicit retention-related supervised signals. To model these signals, we design a Forward Supervised Signals Extractor (FSSE) that utilizes a heterogeneous graph, enhancing the reliability of user retention. To mitigate randomness and uncertainty, we propose a Backward Supervised Signals Stabilizer (BSSS) that utilizes overlooked label-part behaviors within each training window to retrospectively guide the training process. Offline and online evaluations of an industrial system verify the effectiveness of our methods.

Recommendation usually focuses on immediate accuracy metrics like CTR as training objectives. User retention rate, which reflects the percentage of today’s users that will return to the recommender system in the next few days, should be paid more attention to in real-world systems. User retention is the most intuitive and accurate reflection of user long-term satisfaction. However, most existing recommender systems are not focused on user retention-related objectives, since their complexity and uncertainty make it extremely hard to discover why a user will or will not return to a system and which behaviors affect user retention. In this work, we conduct a series of preliminary explorations on discovering and making full use of the reasons for user retention in recommendation. Specifically, we make a first attempt to design a rationale contrastive multi-instance learning framework to explore the rationale and improve the interpretability of user retention. Extensive offline and online evaluations with detailed analyses of a real-world recommender system verify the effectiveness of our user retention modeling. We further reveal the real-world interpretable factors of user retention from both user surveys and explicit negative feedback quantitative analyses to facilitate future model designs. The source codes are released at https://github.com/dinry/IURO.

Real-world super platforms such as Google and WeChat usually have different recommendation scenarios to provide heterogeneous items for users' diverse demands. Multi-domain recommendation (MDR) is proposed to improve all recommendation domains simultaneously, where the key point is to capture informative domain-specific features from all domains. To address this problem, we propose a novel Adversarial feature translation (AFT) model for MDR, which learns the feature translations between different domains under a generative adversarial network framework. Precisely, in the multi-domain generator, we propose a domain-specific masked encoder to highlight inter-domain feature interactions, and then aggregate these features via a transformer and a domain-specific attention. In the multi-domain discriminator, we explicitly model the relationships between item, domain and users' general/domain-specific representations with a two-step feature translation inspired by the knowledge representation learning. In experiments, we evaluate AFT on a public and an industrial MDR datasets and achieve significant improvements. We also conduct an online evaluation on a real-world MDR system. We further give detailed ablation tests and model analyses to verify the effectiveness of different components. Currently, we have deployed AFT on WeChat Top Stories. The source code is in https://github.com/xiaobocser/AFT.

In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display, video, or social. Given a set of audiences or customers (seed users), the audience expansion technique (look-alike modeling) is a promising solution to identify more potential audiences, who are similar to the seed users and likely to finish the business goal of the target campaign. However, look-alike modeling faces two challenges: (1) In practice, a company could run hundreds of marketing campaigns to promote various contents within completely different categories every day, e.g., sports, politics, society. Thus, it is difficult to utilize a common method to expand audiences for all campaigns. (2) The seed set of a certain campaign could only cover limited users. Therefore, a customized approach based on such a seed set is likely to be overfitting.