3,969 research outputs found
Solving the Cold-Start Problem in Recommender Systems with Social Tags
In this paper, based on the user-tag-object tripartite graphs, we propose a
recommendation algorithm, which considers social tags as an important role for
information retrieval. Besides its low cost of computational time, the
experiment results of two real-world data sets, \emph{Del.icio.us} and
\emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity.
Especially, it can obtain more personalized recommendation results when users
have diverse topics of tags. In addition, the numerical results on the
dependence of algorithmic accuracy indicates that the proposed algorithm is
particularly effective for small degree objects, which reminds us of the
well-known \emph{cold-start} problem in recommender systems. Further empirical
study shows that the proposed algorithm can significantly solve this problem in
social tagging systems with heterogeneous object degree distributions
Ground-state configuration space heterogeneity of random finite-connectivity spin glasses and random constraint satisfaction problems
We demonstrate through two case studies, one on the p-spin interaction model
and the other on the random K-satisfiability problem, that a heterogeneity
transition occurs to the ground-state configuration space of a random
finite-connectivity spin glass system at certain critical value of the
constraint density. At the transition point, exponentially many configuration
communities emerge from the ground-state configuration space, making the
entropy density s(q) of configuration-pairs a non-concave function of
configuration-pair overlap q. Each configuration community is a collection of
relatively similar configurations and it forms a stable thermodynamic phase in
the presence of a suitable external field. We calculate s(q) by the
replica-symmetric and the first-step replica-symmetry-broken cavity methods,
and show by simulations that the configuration space heterogeneity leads to
dynamical heterogeneity of particle diffusion processes because of the entropic
trapping effect of configuration communities. This work clarifies the fine
structure of the ground-state configuration space of random spin glass models,
it also sheds light on the glassy behavior of hard-sphere colloidal systems at
relatively high particle volume fraction.Comment: 26 pages, 9 figures, submitted to Journal of Statistical Mechanic
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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