基于协同过滤的简单餐厅推荐

Umar Farooque, Bilal Khan, Abidullah Bin Jun, Akash Gupta
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引用次数: 5

摘要

协同过滤在设计简单而高效的推荐系统中变得非常流行。基于协同过滤的推荐系统基本上是根据生成的分数和计算出的用户之间的相关性来预测用户对某个项目的兴趣。本文提出了一种基于协同过滤(基于用户)的推荐系统的基本结构和设计步骤,并结合数据的分区和聚类应用,设计了一个餐厅推荐系统。提出的系统降低了复杂性,并给出了从头构建推荐系统的基本方法的清晰视图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Filtering based simple restaurant recommender
The use of Collaborative Filtering is becoming very popular in designing a simple yet efficient recommender system. A recommender system based on Collaborative Filtering basically predicts a user's interest in some item on the basis of the scores generated and the correlation calculated between the users. In this paper we propose a basic structure and steps of designing a recommender system that uses Collaborative Filtering (user based) along with applications of partitioning and clustering of data, thus designing a Restaurant Recommender System. The proposed system reduces the complexity and gives a clear view of the basic approach to build a recommender system from scratch.
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