A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks
A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks
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2014
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eng
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application/pdf
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5 pages
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18th International Computer Science and Engineering Conference (ICSEC 2014), 273-277
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Abstract
This paper aims to present and discuss the similarity
among users in a social network based on CF (Collaborative
Filtering) algorithm and SimRank (Similarity Based on Random
Walk) algorithm. The CF algorithm used to predict the relationship
between users based on the user rating on items (movies and books)
and the user’s profile. The SimRank algorithm calculates the
similarity among users through finding the nearest neighbors for
each user in the social network. At last, the combination of these
two algorithms will be used to get “people may interest each other”
from users’ database. In the experimental analysis, a data set
“DouBan” (a data set is collected from a Chinese website) will be
used and demonstrates the performance of the improved technique
with a website. And the website will be developed to show the
recommended processing of the proposed algorithm. Finally, the
recommendation accuracy of the proposed method is evaluated by
comparing with the existing recommendation algorithms.