Semantic IDs (SIDs) are hierarchical item identifiers learned via residual quantization, offering a promising solution to cold-start recommendation, yet their integration into discriminative recommendations remains challenging and lacks systematic investigation. We present Pin-SCALE, a framework for optimized end-to-end integration of SID into dense retrieval in cascading recommender systems. Pin-SCALE instantiates the principles through three technical contributions: (1) cascading pooling preserving residual quantization hierarchy that outperforms common pooling schema and attention;(2) engagement-aware tokenization that encodes collaborative signals to fully utilize both content and user engagement pattern; and (3) multi-view contrastive learning that harnesses query-item and item-item co-occurrence to align SID representations with engagement prediction. Through extensive offline analyses, we collected systematic guidance and proved the effectiveness of Pin-SCALE. Furthermore, the framework has been tested online on multiple surfaces at Pinterest including Closeup, Home Feed and Search, with different content types like organic and e-commerce. Pin-SCALE achieves substantial improvements in fresh engagements (+3.67% repins) as well as platform-level retention (+0.05% DAU). Our work has been shipped into production to serve billions of requests daily and drive discovery journey of our users, as well as provides principled guidelines for SID adoption in discriminative recommendation models at production scale.

Pinterest is the visual discovery platform where people find inspiration, curate ideas, and shop products for all life's moments. An intentful journey can start when Pinners (users) click on Pins and arrive at the Closeup surface, where they can continue to explore or refine their intent by browsing related content powered by our visual search and related recommendation engines. Product Pins, or contents that are linked to merchants and are buyable in general, are critical to realizable fulfillment in Pinners' exploratory journey. This paper focuses on optimizing embedding-based retrieval (EBR) to retrieve relevant and personalized product Pins to drive actionable engagement. In contrast to conventional EBR systems, we introduce complementary Shopping Priority Corpora that are prepared by probability models from a dynamic inventory with billions of candidates and highly skewed distributions. This novel design enabled us to significantly improve the retrieval efficiency, scale up the systems, and meet different business requirements. On top of that, we build retrieval models that infuse multimodal information with multi-task contrastive learning to balance relevance and engagement. We evaluate the EBR system on random off-policy traffic with thorough baseline comparisons and rigorous online A/B experiments. This work leads to significant metric gains in our production systems and provides practical lessons on improving early retrieval for multiple business objectives at large scales.

BACKGROUND: Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations. RESULTS: In this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer. CONCLUSIONS: Parameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships.

With the development of wireless communications and embedded systems technologies, many smartphones are equipped with a multitude of sensors such as GPS and powerful computational, storage and communication capabilities. By these smartphones, location-based services provide the potentialto understand people's mobility pattern at an unprecedented level. How to discover people's personal mobility patterns is key phase for location-based services to provide high-quality service. Therefore, we focus on mining popular mobility patterns from user GPS trajectories. Firstly, we propose a transform method that transforms a GPS trajectory into a sequence of POI(points of interest) based on the spatial and temporal property of GPS points. Then, we propose a periodic-frequent POI sets mining method to discover the POI sets which are not only occurring frequently, but also appearing periodically. Finally, experimental results show the efficiency and stability of the algorithm.