Recently, personalized recommender systems have become indispensable in a
wide variety of commercial applications due to the vast amount of overloaded
information. Network-based recommendation algorithms for user-object link
predictions have achieved significant developments. But most previous
researches on network-based algorithm tend to ignore users’ explicit ratings
for objects or only select users’ higher ratings which lead to the loss of information and even sparser data. With
this understanding, we propose an improved network-based recommendation
algorithm. In the process of reallocation of user’s recommendation power, this
paper originally transfers users’ explicit scores to users’ interest similarity
and user’s representativeness. Finally, we validate the proposed approach by
performing large-scale random sub-sampling experiments on a widely used data
set (Movielens) and compare our method with two other algorithms by two
accuracy criteria. Results show that our approach significantly outperforms other
Cite this paper
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