Probabilistic Conditional Preference Network Assisted Interactive Genetic Algorithm and Its Application
[1]Sun Xiaoyan,Zhu Lixia,Chen Yang.Probabilistic Conditional Preference Network Assisted Interactive Genetic Algorithm and Its Application[J].Journal of Zhengzhou University (Engineering Science),2017,38(06):1-5.[doi:10.13705/j.issn.1671-6833.2017.06.001]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
38
Number of periods:
2017 06
Page number:
1-5
Column:
Public date:
2017-11-20
- Title:
- Probabilistic Conditional Preference Network Assisted Interactive Genetic Algorithm and Its Application
- Keywords:
- interactive genetic algorithm; uncertainty; possibilitic conditional preference networks; personal-ized search
- CLC:
- -
- Abstract:
- Interactive evolutionary algorithms with user preference implicitly extracted from interactions of user are more powerful in alleviating user fatigue and improving the exploration in personalized search or recommendation. However, the uncertainties existing in user interactions and preferences have not been considered in the previous research, which will greatly impact the reliability of the extracted preference model, as well as the effective exploration of the evolution with that model. Therefore, an interactive genetic algorithm with probabilistic conditional preference networks (PCP-nets)is proposed , in which, the uncertainties are further figured out according to the interactions, and a PCP-net is designed to depict user preference model with higher accuracy by involving those uncertainties. First, the interaction time is adopted to mathematically describe the relationship between the interactions and user preference, and the reliability of the interaction time is further defined to reflect the interactive uncertainty.The preference function with evaluation uncertainty is established with the reliability of interaction time. Second, the preference weights on each interacted object are assigned on the basis of preference function and reliability. With these weights, the PCP-nets are designed and updated by involving the uncertainties into the preference model to improve the approximation. Third, a more accurate fitness function is delivered to assign fitness for the individuals. Last, the proposed algorithm is applied to a personalized book search and its superiority in exploration and feasibility is experimentally demonstrated.
- References:
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