基于UTFB算法的污染源信息推荐

Pollution Source Information Recommendation Based on UTFB Algorithm

摘要: 由于污染给社会生活带来了诸多困扰，以及污染源的固有特性，作为污染源信息需求者的环境保护机构和个人，从大量污染源信息中找到自己关注的信息往往不是一件容易的事情；而对于污染源信息提供者，让自己的信息为广大用户所关注，也是一件非常困难的事情。推荐系统就是解决这一矛盾的主要工具。通过建立分析用户喜好模型，采用UFTB算法从用户看过的污染源信息及其信息类型入手，对用户看过的污染源信息类型与评分数据进行分析。在建立分析污染源信息推荐模型中，采用协同过滤算法计算修正后的余弦相似度，对缺省值进行预测以优化算法。为防止过度优化，采取剔除用户非喜好类型污染源信息，得到优化缺省值预测矩阵，将相似度数据带入推荐公式得出数值并使用排序，找出与目标用户相似度最高的N个用户，根据它们的喜好对目标用户进行污染源信息推荐。

Abstract: Because pollution has brought a lot of trouble to social life, as well as the inherent characteristics of pollution sources, as the source of pollution information needs of environmental protection institu-tions and individuals, from a large number of pollution source information to find their own concern of the information is often not an easy thing. The recommendation system is the main tool to solve this contradiction. By establishing the model of analyzing user preferences, UFTB algorithm is used to analyze the type of pollution source information and scoring data that users have seen. In estab-lishing the recommendation model for analyzing pollution source information, the modified cosine similarity is calculated by using the co-filter algorithm, and the default value is predicted to opti-mize the algorithm. In order to prevent over-optimization, we should take the information of elimi-nating the user's non-preferred type of pollution source, get the optimization default prediction matrix, bring the similarity data into the recommended formula to get the value and use the sort, find the N user with the highest similarity to the target user, and recommend the target user the pollution source information according to their preferences.