| dc.contributor.author | Singh, Manish | |
| dc.description.abstract | Every day we are overwhelmed with choices and options. Recommendation systems have gained popularity in providing suggestions. But every web application today has its own recommendation system. The recommendations provided by these systems are generic than user specific. Also the consumers of these systems are facing with the challenge of trusting these resources as they come from anonymous users.
In this paper we propose a social networking based collaborative filtering recommendation system for movies. The prediction rating for a movie is provided based on similarity between you and your friends in the recommendation system. We have used two approaches to derive the similarity function. In the first approach, similarity is achieved using cosine-vector similarity function. And in the second approach, we have used Pearson Correlation Coefficient. These similarity function results are then used to compute the final prediction rating for a user. Finally result from both the approaches is compared. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Social Network Based Recommendation System | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2013-12-02T22:05:17Z | |
| dc.date.available | 2013-12-02T22:05:17Z | |
| dc.date.issued | 2013 | |
| dc.identifier.uri | http://hdl.handle.net/10365/23098 | |
| dc.subject.lcsh | Motion pictures -- Ratings -- Data processing. | en_US |
| dc.subject.lcsh | Online social networks. | en_US |
| dc.subject.lcsh | Recommender systems (Information filtering) | en_US |
| dc.subject.lcsh | Correlation (Statistics) | en_US |
| dc.subject.lcsh | Data mining. | en_US |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Nygard, Kendall | |
