Review on State-of-the-art Technologies and Algorithms on Recommendation System

DOI

10.2991/icmeit-16.2016.46How to use a DOI?

Keywords

Hybrid filtering; Collaborative filtering; Sentiment analysis; Recommendation system; Evaluation.

Abstract

With the rapid development of Internet technology, we have entered into an era of information explosion. An obvious feature of this era is that it has a huge amount of data information. Facing with the huge amount of information, we need a system to effectively screen and filter the large-scale data. If a system can display the helpful information as much as possible, then users can save time to filter information. Therefore, how to design a system that can effectively screen important information and filter secondary information has become an important research topic in the era of big data. However, with the rapid development of the network and a large number of online information, there is a problem of information overload. A new way to solve the problem of information overload is to design and implement a recommendation system. The recommendation system can recommend information and products that interest users, according to user's information needs, interests, et al. In this paper, we introduce several recommendation systems based on different methods and algorithms, and we compare the effectiveness of assessments. The quantitative results of the user's emotional factors are applied into scoring matrix and similarity calculation in order to improve the effectiveness and robustness of recommendation system. When collaborative filtering based recommendation system combined with sentiment analysis, we can obtain an effective recommendation system.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haoyang Li
AU  - Yuanxu Wu
AU  - Wei Xia
PY  - 2016/08
DA  - 2016/08
TI  - Review on State-of-the-art Technologies and Algorithms on Recommendation System
BT  - Proceedings of the 2016 International Conference on Mechatronics Engineering and Information Technology
PB  - Atlantis Press
SP  - 245
EP  - 251
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeit-16.2016.46
DO  - 10.2991/icmeit-16.2016.46
ID  - Li2016/08
ER  -