Guobing Zou

Neighborhood-User Profiling Based on Perception Relationship in the Micro-blog Scenario

In the micro-blog scenario, personal user profiling relying on content is limited for recommending desired diverse subjects due to its shortcomings of short text, often leading to a poor recall. Currently, many methods only utilized the personal knowledge from each individual user to represent user profile without considering the neighborhood information. However, resource information related to neighboring friends play an important role in improving the performance of recommender systems. In this paper, we present the personalized expanded user profiling for micro-blog subject recommendation via ontology semantics structure. Next, taking into account diffusion ability of followee friends, we discuss resource perception relationship (RPR) and follow perception relationship (FPR). Finally, we discuss how, by adjusting the importance of RPR and FPR, the neighborhood is selected to construct neighborhood-user profile, which can mine new relevant subjects for target user. Our experimental results demonstrate the effectiveness of our neighborhood-user profiling in comparison to the existing collaborative filtering and personal user profile recommendation approaches on Sina micro-blog platform datasets.

Overlapping Community Detection in social network based on Microblog User Model

Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping communities in social networks, and its novelty lies in that it combines with the Microblog User Model (MUM) which can reflect the interest of the user accurately. First, the MUM network, which is an undirected and weighted network, is constructed by computing the similarity among MUMs. Afterwords, Overlapping Community Detection based on MUM (OCD-MUM) is performed to partition the network. A community stops expanding when the fitness function reaches a local maximum. The communities detected are locally optimized. A users interest is not only decided by the MUM, but it is also affected by the communities the user belongs to. The community model can reflect the interest of the community. The MUM is updated with community model of its communities, and therefore the interest of the user can be predicted by these communities. Our experiment result shows that OCD-MUM has a higher modularity Q value than traditional methods and the predicted interest is more close to the real world situations.

The Dynamically Efficient Mechanism of HDFS Data Prefetching

In recent years, along with cloud computing developing as a widely used computing paradigm, Hadoop Distributed File System (HDFS) has become one of the mandatory techniques, which has many important features, such as master and slave construction of HDFS, direct client accessing, and multi-duplicate of each data block. All of these make HDFS data prefetching much harder than the traditional data acquisition approaches. Moreover, the basic problems of HDFS data prefetching mainly include what kind of data to prefetch, where to prefetch data, how many data to prefetch, and the balance of prefetching data services and normal data access conflicts. Under above analysis, this paper tries to solve these problems and propose the mechanism of the two-layer HDFS data prefetching. The experimental results show that the Hadoop platform which offers data prefetching mechanism can improve 60% of whole performance on data prefetching.

Towards optimal discovery of web services for multiple QoS constraints and preferences

Web service discovery (WSD) is the task of matchmaking a set of relevant web services. Quality of service (QoS) has recently been applied to represent non-functional properties of web services. Therefore, when those services provide the same functionality but have different QoS values, how to effectively filter out the services that cannot satisfy the QoS constraints and rank the remaining services is still an open research issue. In this paper, we propose an integrated approach that allows a service requester to specify a functionality request, multiple QoS constraints and their preferences, and our method discovers a set of the most appropriate ranked services with QoS utility aggregation. By conducting empirical experiments on simulated web services, we validate the feasibility of our service discovery approach. The running example shows that, our approach can find more appropriate services by the satisfiability of multiple QoS constraints and the ranking of aggregated QoS.

Research on life-cycle of user model in U-Business

Abstract“U-Business” is a novel type business environment, which can provide various services via many mobile devices. In order to provide personalized service to different users, user model (UM) can play an important role in U-Business. UM reflects some characteristics of users to a certain degree, which is used widely in U-Business, like personalized recommendation, social computing, information retrieval services, and so on. Currently, there are more and more researchers who focus on the building and update of UM based on the activities of people. However, as too many UM appeared, the number of UM in cyber space is increasingly large, which takes a lot of space and cost. Furthermore, after some users disappear in the physic world, their models are still working in the cyber world. This case is not reasonable obviously, but few researches take care about it. Therefore, one of important issues, the death of UM should be taken into account in the whole life-cycle of user model. This paper proposes a specific user modeling method for the Cyber Individuals (Cyber-I) in U-Business. The essential difference between this UM and traditional ones is that it has a life, that is, birth, growth, and demise, like a life-cycle of Cyber-I. Specially, the significance of UM life ending and five states of UM death are described from an organic viewpoint. In addition, there is a framework of the whole life process of UM. Finally, the proposed idea is applied to the field of personalized service.

Multi-granularity Recommendation Based on Ontology User Model

The traditional personalized recommendation system supplies the target user with top k items in fixed interest subject. However, the recommended items cover the coarse subject level and the accuracy performance is poor. Taking into account ontology structure of subject, users actual interests can distribute in multiple sub-subject structures. In this paper, multi-granularity recommendation mechanism relying on multi-granularity similarity is proposed to fit users actual detail demands. Specially, a personalized ontology user model is learned to represent users multi-granularity interests. According to ontology structure, the multi-granularity similarity method is implemented by combing content closeness and semantic closeness between user models at different grained subjects. Lastly, recommendation method distributed in multi-granularity subjects is achieved to compare against traditional single subjects recommendation for their performances. The experimental results show that the proposed mechanism is more successful.

MaaS: Model as a Service in Cloud Computing and Cyber-I Space

In recent years, there has been an increasing interest in user modeling, because of its extremely wide usage in real world applications, such as recommendation systems and personalized services. However, a large number of researchers have been devoted to focusing on developing a user model that can only facilitate their own applications, which has limited the rapid advances in user modeling research, so that ubiquitous modeling theory and its applications have become an emerging and open issue to solve. To handle this challenging task, this paper investigates the existing user modeling techniques and potentially applicable areas, including cloud computing and Cyber-Individual (Cyber-I), which are on the way of gradually evolving as important computing paradigms. This paper first proposes a novel user modeling conception and profile from Service-Oriented Architecture (SOA) point of view, called Model as a Service (MaaS). Then, based on the widely used three-layer abstraction of cloud computing, a new MaaS-based four-layer new architecture of cloud computing is proposed, allowing users and model developers to participate in cloud activities for the purpose of supporting personalized services. Finally, a novel MaaS-based Cyber-I framework is proposed on the basis of the architecture of Cyber-I oriented platform, where user modeling and MaaS-related operations center on the functionalities, providing the services of creation, evolution and retrieval in Cyber-I space.

Overlapping community detection in heterogeneous social networks via the user model

Abstract Clustering users with more common interests who interact frequently on social networking sites has attracted much attention from researchers due to the high economic value and further application prospects. Community detection is a widely accepted means of dealing with the challenge of clustering users, but conventional methods are inadequate since there are billions of vertices and various relations in social media. Through the user model, a heterogeneous network containing both undirected and directed edges is built in this study to exactly simulate a social network. A novel approach for overlapping community detection in a heterogeneous social network (OCD-HSN) is proposed, which contains seed selecting and community initializing and expanding to accurately and efficiently unfold modules in parallel. Experimental results on artificial and real-world social networks demonstrate the higher accuracy and lower time consumption of the proposed scheme compared with other existing state-of-the-art algorithms.

Socialized User Modeling in Microblogging Scenarios for Interest Prediction

For adapting functionality to individual users, systems need interest prediction information of these users. Social media provides opportunities to gather mass user data for this purpose. To effectively extract user interests, this paper proposes a hybrid user modeling approach which integrates isolated interests discovery via text analysis into the social relationship-based method by community detection for the interest prediction of microblog users. We conduct extensive experiments on large-scale microblogging datasets with 12,746 users. The experimental results demonstrate that our hybrid approach for socialized user modeling can significantly improve prediction accuracy of user interests, in comparison of the text-based method.

Service composition and user modeling for personalized recommendation in cloud computing

In recent years, cloud computing is gradually evolving as a popular computing paradigm, which offers a uniform platform for service providers to publish their applications as cloud services. In many cases, however, single cloud service cannot satisfy a service request due to its simple functionality. Furthermore, current service composition systems have seldom taken into account user interests for personalized recommendation. In this paper, we propose a novel framework for personalized service recommendation in cloud computing platform by Web service composition and user modeling. The proposed framework first models cloud services together with a service request as a Web service composition problem, called cloud service recommendation (CSR) planning problem. It is fed into our self-developed service planner to compose a cloud service with complex business workflow. Second, our framework also applies user modeling for checking whether the generated composite cloud service can be matched with the interests of service consumer. To validate the feasibility of CSR framework, we have designed and implemented two prototype systems, QoS-aware service composition system and service platform based on user model.