A Review of Different Techniques for Recommender Systems |
Author(s): |
| Ashwini N. Mankar ; Prof. Gogate Uttara Dhananjay |
Keywords: |
| Recommender System, Collaborative Filtering Content Based Filtering, Knowledge Based Filtering, Hybrid Filtering, Information Filtering |
Abstract |
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Recommender Systems is considered as a software tool or technique which provides recommendations for items which would be beneficial for the user. The ultimate aim of providing recommendations is to support the user in their several decision-making processes, such as which product to purchase, which type of music they should listen, or what news to read. In the E-commerce user is loaded with lot of information about various items available on the internet, the Recommender systems have recognized to be important mean to help the online users. Different techniques for recommendation generation have been proposed. The techniques for recommender systems can be categorised in four main types they are Collaborative Filtering (CF), Content Based Filtering (CBF), Knowledge Based Filtering (KBF) and Hybrid Filtering (HF). This paper contains an outline of given techniques of recommender systems. |
Other Details |
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Paper ID: NCTAAP157 Published in: Conference 4 : NCTAA 2016 Publication Date: 00/00/0000 Page(s): 677-681 |
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