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| Content Provider | ACM Digital Library |
|---|---|
| Author | Abhari, Abdolreza Ahmed, Lubaid |
| Abstract | This paper presents a framework for the distributed recommender system that can process the huge amount of social data of Web 2.0 sites in online manner. To evaluate this idea a multi-agent simulator is built to exploit user profile together with information collected from user activities. For each user an agent is designed in the simulated Twitter environment that communicates with other agents to find users with similar interests and then provide recommendation to its user. This framework consists of the following tasks: building user profile using multi-agents from a social network website (i.e. Twitter), data cleansing, tweet analysis using Term Frequency / Inverse Document Frequency (TF/IDF), and providing recommendations. The first goal of the proposed distributed recommender system framework is to examine online algorithms that can be used for big social data (i.e. tweets by Twitter users) and cluster users based on their interest. |
| Starting Page | 15 |
| Ending Page | 16 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781510801035 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-04-12 |
| Publisher Place | San Diego |
| Access Restriction | Subscribed |
| Subject Keyword | Multi-agent system Social networks Big data Distributed recommender system |
| Content Type | Text |
| Resource Type | Article |
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