一种新型混合出租公寓推荐系统的设计与实现

IF 0.5 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
A. A. Neloy, S. Alam, R. A. Bindu
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引用次数: 0

摘要

推荐系统(RSs)已成为当今大多数电子商务网站的重要组成部分。虽然已经有一些关于RSs的研究,但仍然需要一个混合推荐系统,让房地产搜索引擎根据用户的喜好找到合适的出租公寓。为了解决这一问题,本文提出了一种混合推荐系统,该系统由两种最流行的推荐方法-协同过滤(CF)和基于内容的推荐(CBR)构建。基于cf的方法使用用户对项目的评分作为学习做出推荐的唯一信息来源。然而,在搜索引擎等应用程序中,这些评级通常非常稀疏,导致基于cf的方法降低了准确性和性能。为了减少CF方法中的这种稀疏性问题,使用了基于CBR模块中的特征向量(FV)的用户和预测公寓之间的余弦相似度评分(CSS)。CF模型的奇异值分解(SVD)与偏置矩阵分解(MF)相结合,CBR模型的CSS与FV相结合,构建了混合推荐系统。建议的推荐人使用由留一验证(LOOCV)组成的统计交叉验证进行评估。实验结果表明,该方法在准确率和召回率方面明显优于基准随机推荐。此外,对准确率和误差最小化之间的关系进行了图形化分析,为该混合推荐系统在该领域的潜力提供了进一步的证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and Implementation of a Novel Hybrid Rental Apartment Recommender System
Recommender Systems (RSs) have become an essential part of most e-commerce sites nowadays. Though there are several studies conducted on RSs, a hybrid recommender system for the real state search engine to find appropriate rental apartment taking users preferences into account is still due. To address this problem, a hybrid recommender system is proposed in this paper constructed by two of the most popular recommendation approaches — Collaborative Filtering (CF), Content-Based Recommender (CBR). CF-based methods use the ratings given to items by users as the sole source of information for learning to make a recommendation. However, these ratings are often very sparse in applications like a search engine, causing CF-based methods to degrade accuracy and performance. To reduce this sparsity problem in the CF method, the Cosine Similarity Score (CSS) between the user and predicted apartment, based on their Feature Vectors (FV) from the CBR module is utilized. Improved and optimized Singular Value Decomposition (SVD) with Bias-Matrix Factorization (MF) of the CF model and CSS with FV of CBR constructs this hybrid recommender. The proposed recommender was evaluated using the Statistical Cross-Validation consisting of Leave-One-Out Validation (LOOCV). Experimental results show that it significantly outperformed a benchmark random recommender in terms of precision and recall. In addition, a graphical analysis of the relationships between the accuracy and error minimization is presented to provide further evidence for the potentiality of this hybrid recommender system in this area.
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来源期刊
Advances in Data Science and Adaptive Analysis
Advances in Data Science and Adaptive Analysis MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
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