Abstract
In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.
Highlights
As one of the four fundamental interactions of nature, the Gravity law was discovered from the well-known Galilei’s dropping ball experiment at the Leaning Tower of Pisa [1]
Experimental Results The empirical data we use in this paper include: (a) MovieLens: one representative website, provided by GroupLens project, where users can vote movies in five discrete ratings 1–5; (b) Del.icio.us: obtained by downloading publicly available data from the social bookmarking website, which allows users to store, organize and retrieve personal bookmarks via social tags
As static network (ST) network is extracted from the real user-object bipartite network, it would naturally keep the original relationship of users’ common interests
Summary
As one of the four fundamental interactions of nature, the Gravity law was discovered from the well-known Galilei’s dropping ball experiment at the Leaning Tower of Pisa [1]. The essential problem of both network evolution and recommender systems is to evaluate the similarity of each unconnected node pair, which is the core function that gravity model can provide. Experimental results on two representative datasets, Del.icio.us and MovieLens, show that the proposed gravity model can significantly enhance the recommendation performance.
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