Large scale distributed cooperative environments on clouds and P2P

Recommender systems provide users with product information and suggestions, which has gradually become an important research tool in e-commerce IT technology, which has attracted a lot of attention of researchers. Collaborative filtering recommendation technology has been the most successful recommendation technology so far, but there are two major problems—recommendation quality and scalability. At present, research at home and abroad mainly focuses on recommendation quality, and there is less discussion on scalability. The scalability problem is that as the size of the system increases, the response time of the system increases to a point where users cannot afford it. Existing solutions often result in a significant drop in recommendation quality while reducing recommendation response time. In this paper, the clustering analysis subsystem based on the genetic algorithm is innovatively introduced into the traditional collaborative filtering recommendation system, and its design and implementation are given. In addition, when obtaining the nearest neighbors, only the clustered users of the target user are searched, making it a collaborative filtering recommender system based on genetic clustering. The experimental results show that the response time of the traditional collaborative filtering recommender system increases linearly with the increase in the number of users while the response time of the collaborative filtering recommender system based on genetic clustering remains unchanged with the increase in the number of users. On the other hand, the recommendation quality of the collaborative filtering recommender system based on genetic clustering is basically not degraded compared with that of the traditional collaborative filtering recommender system. Therefore, the collaborative filtering recommender system based on genetic clustering can effectively solve the scalability problem of the collaborative filtering recommender system.

The concept of time in traditional distributed systems has been inherited in the Computer-Supported Collaborative Work (CSCW) literature. The following assumptions have generally been made: (1) Events are atomic and their durations do not matter. (2) Total ordering of events can be achieved by some mechanical algorithm. (3) The relationship between events is determined solely by time (causal relationship). However, we observe that these assumptions are not appropriate if the goal is to faithfully preserve user intentions in collaborative systems. In particular, we discuss why and how these assumptions should be relaxed or removed in the design of collaborative editing systems.

Collaborative platform on clustering applications for governments consists of six large-scale systems, including the clustering Government Internet portal system, clustering public-mailboxes collaboration system, clustering government affairs portal system, clustering emergency information collaboration system, clustering office automation collaboration system, and clustering messages collaboration systems. The appli-cation and technology architectures of the collaborative platform are elaborated in this paper，and the major key technologies on the platform are also expounded, which includes realization of many governments ap-plications’ scale integration and collaborative application, business model driven software development plat-form based on SOA, SSO, tans-departmental and cross-level multi-engine clustering protocol. Based on the "clustering application" design, to maximize the utilization of hardware, software resources and administra-tive resources of the provincial government collaborative platform, rural districts and counties can build their own platforms based on the provincial platform. The platform having been running for over 2 years shows that planning of urban and rural e-governments’ construction and maintenance is achieved, thus reducing costs greatly and improving governments’ functions.

Currently the use of web recommendation techniques is growing worldwide with aim of providing the custom-made required data to end users. Most of the web users complain about finding useful information on web sites. Web recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Recommender systems have been extensively explored in web mining. The web recommendation systems are split into two main categories such as collaborative recommendation system and content based recommendation system. However, the quality of recommendations and the user satisfaction with such systems are still not optimal. In adaptive association recommender systems based on web mining techniques have strengths and weaknesses. One of these weaknesses is lack of consideration of an appropriate measure to calculate the user’s interest degree of pages. Applying Fuzzy Association Rule Mining (FARM) algorithm to a recommender system leads to calculate user’s interest of pages more accurately. Consequently generated recommendations will be more desirable when compared to other association rule mining techniques.

As computer systems are evolving and coming to be regarded as social actors, the importance of social intelligence that enables natural and socially appropriate interactions is gaining a growing interest among the human-computer interaction researchers. This article discusses the definition, importance, and benefits of social intelligence as agent technology. It then describes a collaborative learning system that incorporates agents that are equipped with a social intelligence model. We argue that socially appropriate affective behaviors provide a new dimension for collaborative learning systems. The system provides an environment in which learning takes place through interactions with a coaching computer agent and a co-learner, an autonomous agent that makes affective responses. The social intelligence model that handles affective responses is based on psychological theories of personality, emotion, and human-media interaction, such as appraisal theory and the Media Equation. Experiments conducted with this collaborative learning system to examine the effect of the social intelligence model suggested that users had more positive impressions about the usefulness, the application, and their learning experience when the co-learner agent displayed social responses with personality and emotions than when it did not express them. It should be noted here that the co-learner agent did not provide any explicit assistance for the learner, such as giving clues and showing answers, yet it influenced the user’s evaluation on the usefulness of the learning system. Experimental data also suggest that the co-learner agent contributed to the effectiveness of the learning system.

Computer Supported Collaborative Work (CSCW) has been greatly enhanced by technology that provides almost real-time capture, replay, and sharing of content. “The cloud” has popularized ease of deployment for apps serviced through a centralized approach. In this work we propose a more aggressive design, decentralizing the location of shared content within a peer-to-peer structure. Ease of deployment is maintained through a novel architecture for a multilayer distributed cloud. This paper describes how this approach has shown promise in a prototype app, ThinkTogether, and the potential to enable a consistent user experience, even for remote peers sharing VoIP.

Recommender systems are widespread due to their ability to help Web users surf the Internet in a personalized way. For example, collaborative recommender system is a powerful Web personalization tool for suggesting many useful items to a given user based on opinions collected from his neighbors. Among many, similarity measure is an important factor affecting the performance of the collaborative recommender system. However, the similarity measure itself largely depends on the overlapping between the user profiles. Most of the previous systems are tested on a predefined number of common items and neighbors. However, the system performance may vary if we changed these parameters. The main aim of this paper is to examine the performance of the collaborative recommender system under many similarity measures, common set cardinalities, rating mean groups, and neighborhood set sizes. For this purpose, we propose a modified version for the mean difference weight similarity measure and a new evaluation metric called users’ coverage for measuring the recommender system ability for helping users. The experimental results show that the modified mean difference weight similarity measure outperforms other similarity measures and the collaborative recommender system performance varies by varying its parameters; hence we must specify the system parameters in advance.

Los sistemas de recomendación han tenido un rápido crecimiento tanto en investigación académica como en aplicaciones industriales en los últimos años. Las recomendaciones se nos proporcionan cuando elegimos elementos como música, películas, restaurantes, noticias, etc. Como parte de las técnicas de recomendación más prometedoras, los sistemas basados en recomendación de filtros colaborativos desempeñan un papel importante en las aplicaciones actuales. La aparición de nuevas fuentes de información requiere la combinación de sistemas de recomendación con técnicas avanzadas de otros campos, como el análisis de redes sociales, el procesamiento del lenguaje natural, el aprendizaje en profundidad, etc. Esta combinación mejora la calidad de las recomendaciones. Los sistemas de recomendación anteriores se centran principalmente en las mejoras de la precisión de la recomendación. Recientemente, surge una tendencia para la evaluación del rendimiento de los sistemas de recomendación que toma muchas propiedades nuevas en consideración. La fiabilidad es uno de los aspectos más notables y será profundamente estudiado en esta tesis. Se han propuesto algunas medidas para medir la fiabilidad de los sistemas de recomendación de filtrado colaborativo basados en la memoria. Sin embargo, observamos un área sin cubrir en el diseño de las medidas de fiabilidad adecuadas para los sistemas de recomendación de filtros colaborativos basados en modelos, especialmente aquellos basados en la factorización matricial. También encontramos que hay una falta de medidas de calidad de fiabilidad adecuadas. Esta tesis se concentra en proporcionar soluciones a ambas ausencias. Después de un estudio exhaustivo de trabajos de investigación del estado del arte, proponemos una nueva medida de fiabilidad que supera los desafíos de generalización y abstracción causados por los factores latentes de los métodos de factorización matricial. La medida propuesta se compara con con métodos y algoritmos representativos, usando bases de datos publicas, tanto académicas como comerciales. Los resultados experimentales muestran que nuestra nueva medida logra mejoras en la calidad de la recomendación. Además, la medida de fiabilidad propuesta también tiene un impacto muy positivo contra los "Shilling Attacks". Para obtener una mejora respecto a las medidas existentes de calidad de la fiabilidad, también proponemos, hasta donde sabemos, la primera medida de calidad de fiabilidad universal que es capaz de proporcionar valores numéricos para cualquier tipo de sistema de recomendación. Esta medida también se ha probado usando conjuntos de datos públicos reales y ha sido comparada con los métodos de calidad de la fiabilidad existentes. Basándonos en los resultados de los experimentos, concluimos que nuestra medida supera los últimos trabajos de investigación, proporcionando valores de calidad de fiabilidad más precisos e informativos. ----------ABSTRACT---------- Recommender systems have experienced a rapid growth in both academical research and industrial applications in latest years. Recommendations are provided to us when we choose items such as music, movies, restaurants, news, etc. As the most promising recommendation techniques, collaborative filtering recommender systems play an important role in current applications. The emergence of novel information sources requires to combine recommender systems with several advanced techniques coming from other fields like social network analysis, natural language processing, deep learning etc. This combination improves the quality of recommendations. Previous recommender systems research mainly focuses on the improvements of recommendation accuracy. Recently a new trend arises for the performance evaluation of recommender systems that takes many novel properties into consideration. Reliability is one of the most remarkable aspects, which will be deeply studied in this thesis. Some measures have been proposed for measuring reliability of memory based collaborative filtering recommender systems. However, we observe a gap in the design of proper reliability measures for model based collaborative filtering recommender systems, especially those based on matrix factorization. We also found that there is a lack in proper reliability quality measures. This thesis concentrates on providing solutions to both lacks. After a comprehensive study of state-of-the-art research works, we propose a novel reliability measure that overcomes the challenges of generality and abstraction caused by latent factors of matrix factorization methods. The proposed measure is tested against current baselines on public academic and commercial datasets; experiment results show that our novel measure achieves better improvements on recommendation quality. What’s more, the proposed reliability measure also has a very positive impact against shilling attacks. In order to fill in the blank of reliability quality measures, we also propose, as far as we know, the first universal reliability quality measure that is able to provide numerical values for any type of recommender systems. This measure has also been tested on public academic and commercial datasets using current methods. Based on the experiment results we conclude that our quality measure outperforms state-of-the-art research works by providing more accurate and informative reliability quality values.

This paper discusses how a group leader computer tutor may aid students in learning collaborative skills in a co-operative learning environment. However, students need to learn collaborative skills and practice using them. The group leader computer tutor discussed in this paper is designed on the principles of co-operative learning, intelligent tutoring systems and computer-supported collaborative work within an intelligent collaborative learning system (ICLS). The group leader aims to facilitate group work on the task level and to teach students how to use collaborative skills in the discussion level as students work on networked computers in the Jigsaw method of co-operative learning. The ICLS and its group leader were used by two classes at a liberal arts university. Qualitative research shows that the students' co-operative attitudes changed and academic achievement improved from pre- to post-treatment. Students, especially, used the communication skill of openness in comment type discussions. The students enjoyed working collaboratively through the ICLS and their teachers thought that the experience was valuable for them.

In conducting collaborative research involving more than one researcher, a system is needed that can improve the performance of researchers in various ways. This study captures the challenges faced by researchers in collaborative research and defines the requirements for collaborative research systems needed to address these challenges. From the analysis of the needs obtained, the researchers designed a collaboration application interface. Collaboration applications are needed to facilitate communication between researchers, search for articles, share publications, and other applications. The distribution of questionnaires and interviews were conducted to obtain the requirements needed for the development of a collaboration system. This research informs system developers to support the development of collaborative systems. In this study, the level of need for a research collaboration system in the university environment will be analyzed along with an analysis of its needs and the design of the user interface.

Computer-based tools supporting collaborative work are becoming crucial elements to organize the activities of groups of people. Nevertheless, current tools do not enable users to incorporate social regulation aspects. In particular, it is not possible to set-up social rules (and the conditions to execute them) to control the interactions among the members of a workgroup. We explore the main issues on introducing social regulation in computer-supported collaborative work. Based on our regulation model, we propose an infrastructure enabling users to ease the development of regulated collaborative applications. The proposed infrastructure is composed by a framework of regulation components and a component management service.

Collaborative recommender system recommends items to the user based on what actions that other users in the same environment did in the past. This paper focuses on recommending popular places based on behavior of Global Positioning System (GPS) traces to multiple users. Collecting different reviews of popular places from multiple visitors is an explicit way to help Recommender System to rate these places. A Genetic Algorithm is used in predicting the interest of the user for unvisited locations, based on implicit and explicit ratings. Then, Recommender System sorts the rated places in descending order by their rates. The places that have the highest rates in most number of users are recommended by the system. Collaborative Recommender system advises the user to go to the best place via mobile according to other users' preferences in places and the nearest place from user's location. Then the system shows the direction to this place on the map via user's mobile.

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.

In recent years cloud computing has taken off and has become an underlying technology in many online applications and websites, whether using a private or public cloud. At the same time with the increase in globalization, online collaborative tools have become very important in order to allow different businesses to communicate with each other, or even different parts of the same company to communicate more easily and share data. Previous work introduced an architecture for a geographically distributed, cloud based collaboration application. While the architecture allowed the scaling up and down of the number of cloud instances being used to host media servers, no control scheme for such cloud scaling was provided. In this paper a model and control scheme for cloud scaling for a collaboration application is provided. The model is based on the leaky bucket model commonly used in network control, while the control system is a self-organizing system. A testbed for the architecture is also presented and used in order to gather performance data from the collaborative application.

Collaborative filtering recommender systems are essentially information systems which are capable of combining the judgment of a large group of people to make personalized recommendations and thereby alleviate the so-called information overload problem. However,collaborative filtering recommender systems are generally vulnerable to shilling attacks. Attackers can inject carefully chosen profiles into recommender systems in order to bias the recommendation results to their benefits. This may lead to a significant negative impact on the robustness of the systems. The main contribution of this paper is to build a probabilistic model for attack detection in the framework of probabilistic generative model. Experimental results show that this model can effectively detect shilling attacks of typical types.