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\section{Conclusion and Further Work}

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In this article we proposed two strategies for extracting a Bayesian Preference Network from a preference stream and discussed their advantages and disadvantages. We implemented the Greedy Strategy and showed that it is fast and produces satisfactory results using real data.
We also showed that the Greedy Strategy outperforms the accuracy and comparability rate of two baselines well known in the literature.
As a promising  future work, we plan to implement the Heuristic Strategy since 
%for two main reasons: (1) it is expected to have high quality, since it has the potential ability to reach any point in the search space and may even outperforms the quality of the Greedy Strategy (limited by the greedy elimination of edges that form cycles); and (2) 
the complexity analysis showed that it is feasible and tends to be more efficient than the Greedy Strategy in streams with larger numbers of attributes.
We also plan to implement a synthetic  preference stream generator, which will enable testing a data set as large as desired.
