精品文档---下载后可任意编辑一种基于相似性度量的综合推举模型的开题报告Title: A Comprehensive Recommendation Model Based on Similarity MeasurementIntroduction:With the explosive growth of information on the internet, people are facing the challenge of finding relevant and personalized content. To address this issue, recommendation systems have been widely applied in various domains, such as e-commerce, social media, and entertainment. Among the recommendation models, similarity-based methods have been proven to be effective in capturing users' preferences and interests. However, most existing similarity-based models focus on a single type of data (e.g., item attributes, user profile, or interaction history), which may not fully reflect the complexity of users' interests. Therefore, developing a comprehensive recommendation model that integrates multiple types of data and performs similarity measurement across them is desirable. Objectives:The goal of this project is to develop a comprehensive recommendation model that can leverage multiple sources of data to provide more accurate and diversified recommendations. Specifically, the objectives are:1. To investigate the state-of-the-art recommendation models that utilize similarity-based techniques and their limitations.2. To propose a novel comprehensive recommendation model that integrates multiple types of data and performs similarity measurement across them.3. To evaluate the proposed model on real-world datasets and compare it with other state-of-the-art methods in terms of accuracy, diversity, and novelty.Methodologies:The proposed model will consist of three main components: data preprocessing, similarity measurement, and recommendation generation. 精品文档---下载后可任意编辑1. Data preprocessing: ...
