119,274 research outputs found
Scalable and interpretable product recommendations via overlapping co-clustering
We consider the problem of generating interpretable recommendations by
identifying overlapping co-clusters of clients and products, based only on
positive or implicit feedback. Our approach is applicable on very large
datasets because it exhibits almost linear complexity in the input examples and
the number of co-clusters. We show, both on real industrial data and on
publicly available datasets, that the recommendation accuracy of our algorithm
is competitive to that of state-of-art matrix factorization techniques. In
addition, our technique has the advantage of offering recommendations that are
textually and visually interpretable. Finally, we examine how to implement our
technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201
Approximate Closest Community Search in Networks
Recently, there has been significant interest in the study of the community
search problem in social and information networks: given one or more query
nodes, find densely connected communities containing the query nodes. However,
most existing studies do not address the "free rider" issue, that is, nodes far
away from query nodes and irrelevant to them are included in the detected
community. Some state-of-the-art models have attempted to address this issue,
but not only are their formulated problems NP-hard, they do not admit any
approximations without restrictive assumptions, which may not always hold in
practice.
In this paper, given an undirected graph G and a set of query nodes Q, we
study community search using the k-truss based community model. We formulate
our problem of finding a closest truss community (CTC), as finding a connected
k-truss subgraph with the largest k that contains Q, and has the minimum
diameter among such subgraphs. We prove this problem is NP-hard. Furthermore,
it is NP-hard to approximate the problem within a factor , for
any . However, we develop a greedy algorithmic framework,
which first finds a CTC containing Q, and then iteratively removes the furthest
nodes from Q, from the graph. The method achieves 2-approximation to the
optimal solution. To further improve the efficiency, we make use of a compact
truss index and develop efficient algorithms for k-truss identification and
maintenance as nodes get eliminated. In addition, using bulk deletion
optimization and local exploration strategies, we propose two more efficient
algorithms. One of them trades some approximation quality for efficiency while
the other is a very efficient heuristic. Extensive experiments on 6 real-world
networks show the effectiveness and efficiency of our community model and
search algorithms
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