Yuxin Dong

A trust-based probabilistic recommendation model for social networks

Abstract In social networks, how to establish an effective recommendation model is an important research topic. This paper proposes a trust-based probabilistic recommendation model for social networks. We consider the recommendation attributes of products to determine similarity among users. Then inherent similarity among products is taken into account to derive the transition probability of a target node. In addition, trust of products is obtained based on their reputations and purchase frequencies. In order to solve the problem of users׳ cold start, we consider users׳ latent factor to find their latent similar users. Finally, we adopt the Amazon product co-purchasing network metadata to verify the effectiveness of the proposed recommendation model through comprehensive experiments. Furthermore, we analyze the impact of the transition probability influence factor through experiments. The experimental results show that the proposed recommendation model is effective and has a higher accuracy.

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Abstract A trust evolution model plays an important role in ensuring and predicting the behaviors of entities in Internetware system. Most of the current trust evolution models almost adopt expertise or average weight method to calculate entities’ trust incomes, and focus on two strategies (‘full trust’, ‘full distrust’) to analyze trust behaviors. In addition, the researches on dynamics evolution models fail to consider the factor of noise, and cannot effectively prevent free-riding phenomenon. In this paper, a trust measurement based on Quality of Service (QoS) and fuzzy theory by considering timeliness of history data is proposed to improve the accuracy of trust measurement results. Furthermore, a trust evolution model based on Wright–Fisher and the evolutionary game theory is proposed. This model considers multi-strategy and noise problems to improve the accuracy of prediction and adaptability of model in complex networks. Meanwhile, in order to solve the free-riding problem, and improve the trust degree of a system, an incentive mechanism is established based on evolutionary game theory to inspire entities to select trust strategies. The simulation results show that this model has good adaptability and accuracy. In addition, this model can effectively improve network efficiency, and make trust income reach an optimal value, so as to improve trust degree of a system.

A fuzzy clustering algorithm based on evolutionary programming

In this paper, a fuzzy clustering method based on evolutionary programming (EPFCM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (FCM). The cluster validity can be measured by some cluster validity indices. To increase the convergence speed of the algorithm, we exploit the modified algorithm to change the number of cluster centers dynamically. Experiments demonstrate EPFCM can find the proper number of clusters, and the result of clustering does not depend critically on the choice of the initial cluster centers. The probability of trapping into the local optima will be very lower than FCM.

Collaborative Filtering Recommendation on Users’ Interest Sequences

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.

A co-evolutionary algorithm based on mixed mutation strategy for WDP in combinatorial auction

To address computational complexity of winner determination in combinatorial auction, a new co-evolutionary algorithms is developed based on combining mixed mutation with self-organization optimization for finding high quality solutions quickly. Mixed mutation strategy can select adaptively mutation operators which are suitable for discrete space to maintain population diversity, self-organization optimization makes the search to jump out of local optima. This paper investigates two combination methods of mixed mutation and self-organization optimization, the results of experiment show the better performance of the second way (MMSEO2) that self-organization optimization is added to mixed mutation strategy set as a pure mutation operator. We compare the proposed algorithm with current well-known approximate algorithms for winner determination problem, and demonstrate that the proposed algorithm MMSEO2 produces competitive results and finds better solutions than other algorithms for large problem sizes.

Web Service Evaluation Method Based on Time-aware Collaborative Filtering

Web service evaluation approaches predict the reliability of services running in the dynamic and unpredictable Internet environment, which helps the target users find the reliable services. Current approaches of evaluation based on the collaborative filtering lack the consideration of the time-aware effectiveness of the past data. To solve the problem, we proposed a web service evaluation approach based on the time-aware collaborative filtering, which computes the time-aware effectiveness of the past data and improves the process of the neighbor users finding. The real-world web service Qos dataset is conducted and the experimental results show that our approach achieves the lower prediction errors than other approaches.