基于显式反馈机制的智能推荐系统模型

O. Temitope.
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引用次数: 0

摘要

推荐系统是智能应用程序,旨在帮助用户在决策过程中,用户想要在潜在的压倒性的替代产品或服务中选择一个项目。这项工作的重点是使用明确显示资金流入和流出的用户银行对账单。使用的数据集是真实可靠的,因为在推荐系统中使用不可靠的数据会导致用户对系统缺乏信任。然而,出于隐私原因,收集的数据是匿名的。推荐系统是采用Java编程语言开发的web应用程序。与其他推荐系统不同,我们使用了面向图形的数据库管理系统。在谷歌新闻中,38%的总浏览量是推荐的结果;同样,Netflix上60%的租赁电影来自于推荐,而亚马逊上35%的销售来自于推荐。亚马逊、eBay、Flipkart等在线公司成功整合推荐系统,促使研究界利用金融领域的类似优势来推荐产品和服务(Lim, 2015)。因此,推荐系统被认为是当今商业中的一种权宜之计。所有推荐系统的目的都是提供能被用户评价和接受的推荐。本文详细描述了采用显式反馈机制为智能推荐系统提供解决方案的方法。本研究工作的方法论是指研究人员采用的研究方法来解决研究问题,如前一章所述。由于任何应用程序的效率和可维护性完全取决于如何准备设计,本章描述了用于实现既定目标的各种过程、方法和程序,以及进行研究的概念结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model of Intelligent Recommender System with Explicit Feedback Mechanism for Performance Improvement
Recommender Systems are intelligent applications designed to assist the user in a decision-making process whereby user wants to choose one item amongst the potentially overwhelming set of alternative products or services. This work focused on using users bank statements that explicitly shows inflow and outflow of funds. The dataset used is real and reliable because the use of non-reliable data in a recommender system causes users lack of trust in the system. However, the data collected were anonymized for privacy reasons. The recommender system was developed as a web application using Java programming language. Unlike other recommender systems, the graph-oriented database management system was used. In Google news, 38% of the total views are the result of recommendations; similarly, 60% of the rented movies from Netflix come from recommendations and more than that Amazon sales percentage due to recommendations are 35%. Successful integration of recommendation system by online companies like Amazon, eBay, Flipkart amongst others impelled the research community to avail similar benefits in financial domain to recommend product and services (Lim, 2015). Therefore, recommendation systems are considered an expedient factor in business nowadays. The aim of all recommender systems is to provide recommendation that will be favourably evaluated and accepted by its users. This work provides detailed descriptions of methods employed to proffer solutions to intelligent recommender system with explicit feedback mechanism. The methodology of this research work refers to the research approach adopted by the researcher to tackle the research problem as stated in earlier chapter. Since the efficiency and maintainability of any application is solely dependent on how the designs are prepared, this chapter describes the various processes, methods and procedures used to achieve set objectives and the conceptual structure within which the research was conducted.
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