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- W1965467830 abstract "Recommender system is a very promising way to address the problem of overabundant information for online users. Though the information filtering for the online commercial systems received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e. low recommendation accuracy for the small degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improve the recommendation performance. Specifically, it not only improve the recommendations accuracy (especially for the small degree items), but also help the recommender systems generate more diverse and novel recommendations." @default.
- W1965467830 created "2016-06-24" @default.
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- W1965467830 date "2012-12-01" @default.
- W1965467830 modified "2023-09-27" @default.
- W1965467830 title "Improving information filtering via network manipulation" @default.
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- W1965467830 doi "https://doi.org/10.1209/0295-5075/100/58005" @default.
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