Building an Effective Recommender System Using Machine Learning Based Framework

被引：0
|
作者：
Ruchika [1 ]
Singh, Ajay Vikram [1 ]
Sharma, Mayank [1 ]
机构：
[1] Amity Univ, Amity Inst Informat Technol, Noida, Uttar Pradesh, India
来源：
2017 INTERNATIONAL CONFERENCE ON INFOCOM TECHNOLOGIES AND UNMANNED SYSTEMS (TRENDS AND FUTURE DIRECTIONS) (ICTUS) | 2017年
关键词：
Machine Learning; Recommender Systems; Apache Mahout; User-Based Collaborative Filtering; Item-Based Collaborative Filtering;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Machine learning forms the base of many information retrieval applications those effect our day to day lives directly or indirectly. One of the Commonly used application of machine learning algorithms is Recommender Systems. Recommender system are information flitering system which takes users rating for items into account and predict user preferences. Many online ecommerce and other categorical websites are able to generate recommendations either on the basis of implicit feedback or explicit feedback. In implicit feedback, preferences are actually based on analysis of browsing patterns of the user, for example, purchase history, web logs etc. Explicit feedback is generated from the ratings provided by the user. In this paper we have shown adaption of collaborative filtering in Apache Mahout platforms via Eclipse on a sample data set.
引用
收藏
页码：215 / 219
页数：5
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