In app recommendation, user cold-start remains a fundamental challenge in recommender systems. Existing approaches primarily focus on efficiently leveraging limited data or transferring knowledge from active users to alleviate the user cold-start problem, yet they often overlook the influence of feature interactions on user cold-start. We group features according to their semantic types and identify an interesting phenomenon: user attribute features and behavioral sequence features interfere with each other, thereby constraining the model's ability to represent cold-start users effectively. We attribute this issue to differences in the latent space distributions and learning complexities of the two feature types, which hinder the model from accurately capturing cold-start users' interests. To address this challenge, we propose the AFIM architecture, which decouples the learning of user attribute and behavior sequential features. AFIM leverages a lightweight attention module to explicitly capture user interests from behavioral sequences, thereby reducing the learning burden on downstream recommendation networks. Additionally, it incorporates feature decoupling and dynamic fusion modules to mitigate learning bias arising from heterogeneous feature spaces. Extensive experiments on two public datasets and two industrial datasets demonstrate that AFIM consistently outperforms SOTA baselines, highlighting its effectiveness in user cold-start scenarios.

Message passing scheme is a general idea for Graph Neural Networks (GNNs) to learn node representations. During message passing, given a target node, we transform and aggregate the feature vectors of its neighbors and generate a representation vector for the target node. However, real-world graph data is usually constructed from complicated scenarios based on manually pre-defined rules; it is often the case that noisy information gets involved in message passing, thereby resulting in sub-optimal performance for GNNs and also impacting their trustworthiness and reliability. In this study, we present an effective learnable threshold technique that explicitly optimizes heterogeneous graph structure with the goal to maximize performance improvement of GNNs for downstream tasks. We give an explanation about the design of the learnable threshold and show the ability that our model can be applied to large-scale graphs. Experiments on seven datasets show that our model has a powerful ability to deal with homogeneous graphs with low homophily ratio and dense graphs. With the verification of robustness analysis, our model can resist the noisy information, which proves the robustness of our model.

Existing online advertising systems employ separate models for each task and site, resulting in a large number of models that require significant computing power and human effort to train and deploy. Moreover, separate models have limitations in sharing cross-scenario information. To address these issues, we propose a unified sequential recommendation model called SCRIPT. It takes cross-scenario user behavior sequences as input and explicitly incorporates meta information that characterizes scenario features, such as domain, site, and behavior types. Inspired by the advances of the pretrain and prompt paradigm, we generate scenario-aware and personalized prompts based on the user profile and meta information of candidate items. This allows the model to leverage the knowledge learned during pre-training and adapt it to serve different downstream tasks. Extensive experiments on two public dataset and a production dataset demonstrate that our model achieves state-of-the-art performance on multiple downstream recommendation tasks.