A Manhattan distance based hybrid recommendation system

Begüm Uyanik, Günce Keziban Orman
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引用次数: 2

Abstract

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.
基于曼哈顿距离的混合推荐系统
许多在线服务提供商使用推荐系统,通过生成推荐来帮助客户做出决策。据此,本文提出了一种新的推荐系统,供旅游客户根据自己需要的功能在网上预订酒店,节省了客户的时间,增加了个性化酒店推荐的影响。这个新系统结合了协作和基于内容的过滤方法,创建了一个新的混合推荐系统。采用RFM (recent, Frequency, Monetary)方法对包含顾客信息和酒店特征的两个数据集进行分析,以便根据顾客的购买性质来识别顾客。推荐系统的主要思想是建立用户和产品之间的相关性,并决定为特定用户选择最合适的产品或信息。由于在线数据呈指数级增长,决策者可以利用旅游行业中使用的大量信息来做出购买决策[20]。过滤、确定优先级和有益地呈现相关信息可以减少这种过载。推荐系统可以通过以下三种主要方式为用户生成推荐列表;基于内容、基于协作和混合方法[1]。本文详细介绍了每个分类及其技术。RFM分析法通过测量顾客的购买习惯来识别顾客细分。它是通过确定客户购买的最近次数、频率和货币价值并在评分模型上对其进行排序来标记客户的过程。评分是基于他们最近购买的时间(Recency)、购买的频率(Frequency)和购买的规模(Monetary)。实验结果表明,与基于协作和基于内容的算法相比,基于曼哈顿距离的混合过滤算法的行为分析精度有很大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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