Chin-Hui Lai

A trustworthy QoS-based collaborative filtering approach for web service discovery

Abstract Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.

Mining the change of event trends for decision support in environmental scanning

As the business environment has become increasingly complex, the demand for environmental scanning to assist company managers plan strategies and responses has grown significantly. The conventional technique for supporting environmental scanning is event detection from text documents such as news stories. Event detection methods recognize events, but neglect to discover the changes brought about by the events. In this work, we propose an event change detection (ECD) approach that combines association rule mining and change mining techniques. The approach detects changes caused by events to help managers respond rapidly to changes in the external environment. Association rule mining is used to discover event trends (the subject patterns of events) from news stories. The changes can be identified by comparing event trends in different time periods. The empirical evaluation showed that the discovered event changes can support decision-makers by providing up-to-date information about the business environment, which enables them to make appropriate decisions. The proposed approach is practical for business managers to be aware of environmental changes and adjust their business strategies accordingly.

Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches

Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A workers document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers’ KFs or the information needs of the majority of a group of workers with similar KFs. A groups needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the groups knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods.

Recommendations based on personalized tendency for different aspects of influences in social media

Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. To resolve this problem, most researches only utilize users’ preferences, the content of items or social influence to make recommendations. However, people’s preferences for items may be affected by social friends, personal interest and item popularity. Moreover, each factor has a different impact on each user. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these factors to recommend photos in a photo-sharing website, Flickr. The personal tendencies related to these three influences are regarded as personalized weights to combine influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.

Group Recommendation Based on the Analysis of Group Influence and Review Content

With the development of internet, users not only receive information passively but also share their own opinions on the social networking websites. Accordingly, users’ preferences for items may be affected by others through opinion sharing and social interactions. Moreover, users with similar preferences usually form a group to share related information with others. Users’ preferences may be affected by group members. Existing researches often focus on analyzing personal preferences and group recommendation approaches without user influence. In this work, we propose a novel group recommendation approach which combines the group influence, rating-based score and profile similarity to predict group preference. The group influence is composed of group member influences, review influence and recommendation influence. The profile similarity is derived from the analysis of item descriptions and review content. The experimental results show that considering the group influence and content information in group recommendation approach can effectively improve the recommendation performance.

Knowledge flow-based document recommendation for knowledge sharing

Knowledge is a critical property that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations have to exploit effective and efficient approaches to help knowledge workers find task-relevant knowledge, as well as to preserve, share and reuse such knowledge. Hence, an important issue is how to discover knowledge flow (KF) from the historical work records of knowledge workers in order to understand their task-needs and the ways they reference documents, and actively provide adaptive knowledge support. This work proposes a KFbased document recommendation method that integrates KF mining and collaborative filtering recommendation mechanisms to recommend codified knowledge. The approach consists of two phases: the KF mining phase and the recommendation phase. The KF mining phase can identify each workers knowledge flow by considering the referencing time and citation relations of knowledge resources. Then, based on the discovered KF, the recommendation phase applies sequential rule mining and the CF method to recommend relevant documents to the target worker. Experiments are conducted to evaluate the performance of the proposed method and compare it with the traditional CF method using data collected from a research institute laboratory. The experiment results show that the proposed method can improve the quality of recommendation.

Recommendations Based on Different Aspects of Influences in Social Media

Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these three decision factors to recommend photos in a photo-sharing website, Flickr. Because these influences have different degrees of impact on each user, the personal tendencies related to these three influences are regarded as personalized weights; combining influence scores enables predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.

Mining knowledge flow for modeling the information needs of task-based groups

Knowledge is the most important resource to create core competitive advantages for an organization. Such knowledge is circulated and accumulated by a knowledge flow (KF) in an organization to support worker’s tasks. Workers may cooperate and participate in several task-based groups to fulfill their needs. In this paper, we propose a group-based knowledge flow mining algorithm which integrates information retrieval and data mining techniques for mining and constructing the group-based KF (GKF) for task-based groups. The GKF is expressed as a directed knowledge graph to represent the knowledge referencing behavior for a group of workers with similar task needs. The frequent knowledge referencing paths are identified from the knowledge graph to indicate the frequent knowledge flows of the workers. We also implement a prototype of GKF mining system to demonstrate the effectiveness of our proposed method.