27,805 research outputs found
Gravity Effects on Information Filtering and Network Evolving
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, \emph{Del.icio.us} and
\emph{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
Regional economic status inference from information flow and talent mobility
Novel data has been leveraged to estimate socioeconomic status in a timely
manner, however, direct comparison on the use of social relations and talent
movements remains rare. In this letter, we estimate the regional economic
status based on the structural features of the two networks. One is the online
information flow network built on the following relations on social media, and
the other is the offline talent mobility network built on the anonymized resume
data of job seekers with higher education. We find that while the structural
features of both networks are relevant to economic status, the talent mobility
network in a relatively smaller size exhibits a stronger predictive power for
the gross domestic product (GDP). In particular, a composite index of
structural features can explain up to about 84% of the variance in GDP. The
result suggests future socioeconomic studies to pay more attention to the
cost-effective talent mobility data.Comment: 7 pages, 5 figures, 2 table
Promoting cold-start items in recommender systems
As one of major challenges, cold-start problem plagues nearly all recommender
systems. In particular, new items will be overlooked, impeding the development
of new products online. Given limited resources, how to utilize the knowledge
of recommender systems and design efficient marketing strategy for new items is
extremely important. In this paper, we convert this ticklish issue into a clear
mathematical problem based on a bipartite network representation. Under the
most widely used algorithm in real e-commerce recommender systems, so-called
the item-based collaborative filtering, we show that to simply push new items
to active users is not a good strategy. To our surprise, experiments on real
recommender systems indicate that to connect new items with some less active
users will statistically yield better performance, namely these new items will
have more chance to appear in other users' recommendation lists. Further
analysis suggests that the disassortative nature of recommender systems
contributes to such observation. In a word, getting in-depth understanding on
recommender systems could pave the way for the owners to popularize their
cold-start products with low costs.Comment: 6 pages, 6 figure
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