3 research outputs found
DOMAIN ABSTRACTION OF HIGHLY CORRELATED PAIRS TO RECOMMEND IN THE LONG TAIL
ABSTRACTAmong difficulties encountered by modern shopping recommenders is the long tail shape of sold items also related to cold-start issues. Various approaches including content-based recommendations attempt to overcome this problem that has serious impact on the accuracy of recommendations especially when new products are continuously added to the catalogue. This paper investigates the use of an algorithm to search for highly correlated pairs between abstractions of items. The advantage of this approach is evaluated on the basis of real data showing better results compared to an approach onlybased on the concrete pairs of items. Using rigorous protocols such as Given-n, experimental results show significant improvement in both the recommendation accuracy and the recommendation of products in the long tail.Keywords. Knowledge Discovery, Mining Correlated Pairs, Recommender Systems
Discovering Dense Correlated Subgraphs in Dynamic Networks
Given a dynamic network, where edges appear and disappear over time, we are
interested in finding sets of edges that have similar temporal behavior and
form a dense subgraph. Formally, we define the problem as the enumeration of
the maximal subgraphs that satisfy specific density and similarity thresholds.
To measure the similarity of the temporal behavior, we use the correlation
between the binary time series that represent the activity of the edges. For
the density, we study two variants based on the average degree. For these
problem variants we enumerate the maximal subgraphs and compute a compact
subset of subgraphs that have limited overlap. We propose an approximate
algorithm that scales well with the size of the network, while achieving a high
accuracy. We evaluate our framework on both real and synthetic datasets. The
results of the synthetic data demonstrate the high accuracy of the
approximation and show the scalability of the framework.Comment: Full version of the paper included in the proceedings of the PAKDD
2021 conferenc