With the increasing diversity and complexity of the Web, skills for online exploration have become more and more important. People typically obtain such skills by gaining experience with classifying and organizing acquired Web pages, creating search queries. However, it is difficult for unskilled people to get the opportunity for meaningful experience. In this research, we aim to develop a mechanism for utilizing the intentions of inexperienced searchers to cultivate exploration skills in realistic way. To achieve this goal, we focused on the attention paid by a searcher to specific pages during the search process. After analyzing this behavior, we developed several methods for extracting clues that embody the searcher’s unaware intentions for use in further exploration. Then, with these methods as a basis, we designed a system for visualizing unaware intentions to promote the experience of exploration in novices. Experimental results using a prototype demonstrate the effectiveness of our methods.

Initial Evaluation of System for Visualizing Novices’ Unaware Search Intentions by Analyzing Behaviors of Article Selection

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy   c  -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

Service Partition Method Based on Particle Swarm Fuzzy Clustering

As the number and variety of services increase, it is becoming difficult and time-consuming to locate services that satisfy users' need. Service clustering is efficacious method to prune the query space, to narrow the searching space, and improve the accuracy of locating services that satisfied users' needs. At present, clustering method of web services adopted single or traditional clustering algorithms. However, accuracy and stability of single or traditional clustering algorithms is poor. In the paper, we proposed SWOC a service clustering method based on wisdom of crowd. Firstly, by using SWOC we calculated document similarity. Secondly, we implemented a mapping algorithm that reduces the correlation of web services and improve accuracy of method. And then, we applyed different number of clusters using different individual clustering methods that increase the number of partitions so as to enhance the robustness of SWOC. Lastly, the diversity algorithm evaluates and selects the partitions to extract interesting information for the final aggregation with the weight of each individual result. Experiments were performed on the real web service dataset crawled from ProgrammableWeb which prove the accuracy, recall, F-value and stability of proposed method.

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A Service Clustering Method Based on Wisdom of Crowds

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A Service Clustering Method Based on Wisdom of Crowds