User interactions on online travel platforms (OTPs) naturally follow a two-stage booking funnel, spanning hotel click from a multi-hotel listing page and booking conversion through room selection within the clicked hotel. Crucially, booking decisions are set-dependent: users select among multiple room types within the same hotel, where price changes of one option can shift demand to others. Two challenges arise in modeling such behavior: (i) the cascading booking-funnel behavior from hotel click to booking conversion, and (ii) intra-hotel substitution across room types. While accurate modeling of such behavior is essential for demand prediction and downstream optimization, existing methods typically ignore this structure by collapsing user behavior into a single stage and overlooking intra-hotel substitution effects, resulting in biased demand estimations and suboptimal decisions. In this paper, we propose BFSNet, a Booking Funnel and Substitution-aware neural network that decomposes booking demand into hotel click and booking conversion, enabling interpretable and stage-wise learning of user behavior. To capture the intra-hotel substitution effect, we introduce a mixing network-based substitution effect module that incorporates economic structural information by enforcing monotonic substitution relationships between the demand of a focal room and the prices of competing room types. To bridge prediction and downstream decision-making, we further develop a lightweight pricing procedure that distills the learned prediction model to efficiently evaluate candidate price vectors under operational constraints. Experiments on large-scale production data from a major online travel platform demonstrate consistent improvements in demand prediction accuracy and significant gains in downstream revenue.

Retrieval Augmented Generation (RAG) with Knowledge Graphs (KGs) is an effective way to enhance Large Language Models (LLMs). Due to the natural discrepancy between structured KGs and sequential LLMs, KGs must be linearized to text before being inputted into LLMs, leading to the problem of KG Alignment with LLMs (KGA). However, recent KG+RAG methods only consider KGA as a simple step without comprehensive and in-depth explorations, leaving three essential problems unclear: (1) What are the factors and their effects in KGA? (2) How do LLMs understand KGs? (3) How to improve KG+RAG by KGA? To fill this gap, we conduct systematic explorations on KGA, where we first define the problem of KGA and subdivide it into the graph transformation phase (graph-to-graph) and the linearization phase (graph-to-text). In the graph transformation phase, we study graph features at the node, edge, and full graph levels from low to high granularity. In the linearization phase, we study factors on formats, orders, and templates from structural to token levels. We conduct substantial experiments on 15 typical LLMs and three common datasets. Our main findings include: (1) The centrality of the KG affects the final generation; formats have the greatest impact on KGA; orders are model-dependent, without an optimal order adapting for all models; the templates with special token separators are better. (2) LLMs understand KGs by a unique mechanism, different from processing natural sentences, and separators play an important role. (3) We achieved 7.3% average performance improvements on four common LLMs on the KGQA task by combining the optimal factors to enhance KGA.

Abstract Triple‐negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple‐negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple‐negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple‐negative breast cancer, and the results show that the triple‐negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple‐negative breast cancer have been obtained. These genes have the following three characteristics: co‐expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis.

The diagnosis of breast cancer in the middle and early period is conducive to later treatment, but the current diagnosis rate is not very desirable. Using machine learning to predict the benign and malignant of breast cancer can provide some assist to doctors' treatment in clinical practice. In this paper, we have collected data from digitized images of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei presented in the image. This work adopts several feature selection methods to select the most related features for breast cancer diagnosis. Based on the selected features, four machine learning models, Support Vector Machine (SVM), Decision Tree (DT), AdaBoost and Random Forest (RF) are built and their performance are evaluated. The experimental results show that the accuracy of RF is higher than the other three methods.