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296 ENTERPRISE INFORMATION SYSTEMS VI according to reviews being associated within those two topics. Thus, in this social network, there is an edge i from a topic v to a topic u, if the owner of topic u is receiving and taking information associated to topic v. In other words, the owner of topic u is in the distribution list of the topic v and takes at least one review containing the topic v and creates a new review on the same document with his/her topic u. Consequently, the graph representation will show the relation v � u. The relation v � u indicates the flow of appreciated information through the network. It means that the owner of topic u is receiving and appreciates information from the owner of topic v. � Online communities � Web technologies b4, b5 + + + + a2, b5, g1, g2 � Java a1, a2, f1 � New technologies g1, g2, g3, Figure 1: Mediated social network example + b5, + � Online communities b5 � Web technologies a2 � Objects + a2 b4, b5 + + + + a2, b5, g1, g2 a2 a2 b5 g1, g2 � Developing + a2 � Java a1, a2, f1 f1 � New technologies g1, g2, g3, f1 � Internet g4 + b5, + Jean-Charles Laurence Layda � Online communities b5 � Web technologies a2 � Objects + a2 b4, b5 + + + + a2, b5, g1, g2 a2 a2 b5 g1, g2 � Developing + a2 � Java a1, a2, f1 f1 � New technologies g1, g2, g3, f1 John � Internet g4 Pascal Michel Layda + b5, + � Online communities b5 � Web technologies a2 � Objects + a2 b4, b5 + + + + a2, b5, g1, g2 a2 a2 b5 g1, g2 � Developing + a2 � Java a1, a2, f1 f1 � New technologies g1, g2, g3, f1 � Internet g4 + b5, + Jean-Charles Laurence Layda b5 a2 � Objects + a2 a2 a2 b5 g1, g2 � Developing + a2 f1 f1 John � Internet g4 Pascal Michel Layda Figure 1 shows a graphical representation of a small part of such a network. In this example, there is six users. Each box shown as folders represents some of the topics of these users. Each relation v � u between topics is presented by a directed lattice. Reviewed information resources are noted with a lower case letter and a number. A label on a lattice means that a resource has been discovered from a review in the source topic. Our socially aware recommender system first takes into account the interest of users and then takes into account the state of the users topics in the social networks. In the first step, it finds the relationships of users with approximate interests (not only commons ones). This means that for instance, we avoid giving only recommendations directly obtained from intersections of appreciated items in the users' profiles, which is generally the strategy of existing systems. This first feature is obtained by our collaborative filtering techniques using repositories of already classified items (Plu, 2003). Second, the user can control the type of contact recommendations s/he is going to receive. This means that a user can define the strategy to rank computed recommendations. This last feature is accomplished by our SocialRank algorithm, which completes our collaborative filtering algorithm. The SocialRank algorithm uses some social properties to filter topics which are candidates for recommendations (those topics initially computed with the collaborative filtering algorithm). The social properties used depend on the information strategy chosen by the users. They are computed by using the SoMeONe's social network described above. By using those social properties as filters, two users with the same interest would not receive the same recommendations of contacts. Thus, this should avoid the traditional problem of "preferential attachment" in network based communication systems (Adar, 2000). The preferential attachment problem rises when most of users communicate with the same very small group of users. Recommending only experts to everyone could lead to this situation. We will see below (see section 5.3.4) how SoMeONe prevents such a situation by letting users choose another information strategy than the "Looking for Experts" strategy. More generally, different "social properties" computed from the social network analysis can be used to choose the contact recommendations in order to influence the way the social network will evolve! Thus, a socially aware recommender system can help to give the social network some interesting global properties depending on the global criteria the designer of a social media wants to optimise. Such interesting properties can be, for instance: a good clustering factor, a small diameter, a good global reciprocity or/and transitivity factor. We assume that some users will be seeking to be recommended to others. Therefore, by using some specific social properties in the recommendation process, we think the recommender system can influence the motivation and participation of the users. In other words, if users know the strategy used by the recommender system, we can assume that some users will try to adapt their behaviour according to it. To be able to test this idea, we have first implemented the computation of some social properties and we have implemented some information strategies using those properties in order to select appropriate contact recommendations. In order to let users to select one of the implemented strategies which best fit their needs we have ascribed “names” and descriptions to them. Here are the three we have already implemented and experimented: – "Looking for Experts". The user only trust credited experts who filter information for him. – "Gathering all". The user want to have the widest coverage of a topic, thus gathering as much information as possible,
– "Going to the sources". The user wants to obtain the newest information rapidly, avoiding users who are acting as intermediaries. We have started with these three strategies but our goal is to look for new ones or improving the existing ones. By default, the "Going to the source" strategy is selected, but users can change it by editing her/his personal profile. This choice can be refined for each personal topic. The formulae related to the computation of the social properties used by each "strategy" are explained in the SocialRank section. 5 COMPUTING CONTACT RECOMMENDATIONS In this section we are going to present the three steps of our recommendation process. Firstly, we describe the collaborative filtering algorithm used to compute potential contact recommendations based on topic similarities using an existing classification of a large amount of URLs. Secondly, we specify the computation of social properties of each topic in the SoMeONe’s social network. Finally, we show how we filter the potential contact recommendations obtained in the first step according to the topic similarities, the social properties of the recommended topics, and the information strategy chosen by users. 5.1 Collaborative Filtering A CONTACT RECOMMENDER SYSTEM FOR A MEDIATED SOCIAL MEDIA The bases of the collaborative filtering algorithm that we have built are presented in (Plu, 2003). It uses URL co-citations analysis. Co-citation is established when two users associate personal reviews to the same documents or to different documents referenced within the same category of a WWW directory. The recommendations of contacts are computed using one or more specialized directories. By directories, we mean repositories of web sites categorized by subject. For our tests we have started with the one provided by the Open Directory Project (http://www.dmoz.org). The collaborative filtering algorithm (CFA) computes similarity between topics. It has to detect the case of two topics having reviews with URLs equal or similar to the URLs classified in the same ODP category. The CFA computes a similarity measure between each topic and each ODP category. Like others do, this similarity measure is based on URLs co-citation analysis. (the URLs to which the reviews inside topics make reference). This similarity measure is computed according to the formula given in (Plu, 2003). The CFA only computes the similarity between topics that do not belong to the same user. Pairs of similar topics noted (t1, t2) for topics labelled t1 and t2, are sorted according to the similarity measure S. Contact recommendations are then computed from those similar topics. 5.2 SocialRank 297 The SocialRank algorithm filters the topic recommendations according to some of their social properties. Having the topics' taxonomy of users, and the distribution list of the topics defined, we are able to extract the social network explained above. We model this directed graph as an adjacent matrix. Each matrix element represents the relationship between two topics. As introduced above, a relationship is established when a user creates new reviews from other reviews received from other users. They thus establish relationships between their topics within the created reviews and the topics of others within the received reviews. To take into account the importance of each relation, each vertex is weighted with a measure W(e,f) representing the number of documents received from topic f and then reviewed with a topic e. We compute a matrix W with each element noted W(e, f), topic e being in the row and topic f in the column of the matrix, for the vertex from f . W(e,f) is computed with the formula: Card* ( e, f) or W(e, f) = 0 if card(e)=0 W( e, f) � card( e) (1) Card*(e,f) counts all the documents having a review with the topic e and a review with the topic f, the review with topic f being older than the review with topic e; card (e) is the total number of reviews with topic e. Using this W matrix, the SocialRank algorithm also computes one square matrix and two vectors of topics: – A vector of experts E, in order to obtain the expert topics. – A redundancy matrix R, in order to obtain redundant topics. – A vector of originals O, in order to obtain original topics. The computation of these matrix and vectors could be obtained by different methods, as clearly explained in (Wasserman, 1994). To identify topics as "experts" we use a common centrality measure of a topic defined recursively according to the centrality of the topics receiving
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A C.I.P. Catalogue record for this
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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