ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

通过集成方法和特征选择增强网络流量攻击识别

Enhancing web traffic attacks identification through ensemble methods and feature selection

本研究利用机器学习技术，基于 CSIC2010 v2 数据集提取 HTTP 轨迹特征，采用 Random Forest 等集成方法进行网络流量攻击识别，相比基线分类器预测准确率提升约 20%，AUC 达 0.989，结合特征选择增强了模型鲁棒性，为保护网络流量提供了实用框架。

RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting

We present the Mim-Solution's approach to the RecSys Challenge 2016, which ranked 2nd. The goal of the competition was to prepare job recommendations for the users of the website Xing.com. Our two phase algorithm consists of candidate selection followed by the candidate ranking. We ranked the candidates by the predicted probability that the user will positively interact with the job offer. We have used Gradient Boosting Decision Trees as the regression tool.

通过CAWAL框架进行大规模Web门户的预测建模与异常检测

Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework

本研究提出基于CAWAL框架整合应用日志与Web分析数据，构建全面数据集并应用于Gradient Boosting和Random Forest等机器学习模型，实现用户行为预测准确率超92%并提升异常检测能力，为大规模Web门户提供高效可靠的解决方案。

ONCE：利用开源和闭源大语言模型提升基于内容的推荐

ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models

本研究提出ONCE方法，结合开源LLM作为内容编码器和闭源LLM通过提示增强训练数据，显著提升基于内容的推荐性能，相对最先进模型提升达19.32%。

Feature Selection and Model Comparison on Microsoft Learning-to-Rank Data Sets

With the rapid advance of the Internet, search engines (e.g., Google, Bing, Yahoo!) are used by billions of users for each day. The main function of a search engine is to locate the most relevant webpages corresponding to what the user requests. This report focuses on the core problem of information retrieval: how to learn the relevance between a document (very often webpage) and a query given by user. Our analysis consists of two parts: 1) we use standard statistical methods to select important features among 137 candidates given by information retrieval researchers from Microsoft. We find th…

Mining Latent Structures for Multimedia Recommendation

Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommender systems. The majority of previous work focuses on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Specifically, only collaborative item-item relationships are implicitly modeled through high-order item-user-item relations. Considering that items are associated with rich contents in multiple modalities, …