Matrix Factorization and Regression-Based Approach for Multi-Criteria Recommender System
Matrix Factorization and Regression-Based Approach for Multi-Criteria Recommender System
复制标题
基于矩阵分解和回归的多标准推荐系统方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Vibhor Kant
中科院分区：
文献类型：
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作者：
Gouri Sankar Majumder;P. Dwivedi;Vibhor Kant
推荐系统（RS）试图解决信息过载的问题，提供最相关的项目，用户从一个大的项目集。协同过滤（CF），一种流行的方法，在建设RS，生成推荐给用户的基础上提供明确的评级社区的用户。目前，许多在线平台允许用户基于多个标准沿着评估物品，并具有总体评级而不是单个总体评级。以前的研究工作表明，考虑这些多标准的推荐评级提高了推荐系统的预测准确性。
Recommender systems (RS) try to solve information overload problem by providing the most relevant items to users from a large set of items. Collaborative filtering (CF), a popular approach in building RS, generates recommendations to users based on explicit ratings provided by the community of users. Currently many online platforms allow users to evaluate items based on multiple criteria along with an overall rating instead of single overall rating. Previous research work has shown that considering these multiple criteria ratings for recommendations improved the predictive accuracy of recommender systems.