ise all values ofd to 1/N Artificial- Agents or t=l.T Game Theory calculate errore calculate B e/(l-ey calculate D计1 nowledge Representation classifier= argmax ∑ Machine Learning ith result class c hilosophy (All ech [Al] D class weight distribution on iteration Vision[All number of classes T number of iterations weak-learn(D, weak learner with distribution D, weak learn error on iteration t error adjustment value on iteration t Content-Based Navigation classifier final boosted classifier alization[hypertext] Figure 3. AdaboostMl boosting algorithm Figure 5. Section of the research paper topic ontology AdaBoostMI has been shown to improve [11] the performance of 5.3 Recommendation algorithm algorithms, particularly for stronger learning Recommendations are formulated from a correlation between the e k-Nearest Neighbour. It is thus a sensible choice users current topics of interest and papers classified as belonging to boo k classifier to those topics. a paper is only recommended if it does not appear in the users browsed URL log, that recommendations 5.2 User profiling algorithm have not been seen before. For each user, the top three interesting The profiling algorithm performs correlation between paper topic topics are selected with 10 recommendations made in total, Papers classifications and user browsing logs. Whenever a research pape are ranked in order of the recommendation confidence before is browsed that has been classified as belonging to a topic, it being presented to the user. Figure 6 shows the recommendation accumulates an interest score for that topic. Explicit feedback or commendations also accumulates interest value for topics. The current interest of a topic is computed using the inverse time Recommendation confidence= classification confidence t weighting algorithm shown in Figure 4 pic interest value Figure 6. Recommendation algorithm opIc Interest T ∑ Interest value(n)/ days old(n) 6. ONTOCOP Interest values Paper browsed=1 The Ontology-based Communities of Practice Identifier (Onto CoPD)[2] is an experimental system that uses the AKT Recommendation followed= 2 ontology to help identifying communities of practice(CoP). The Topic rated interesting= 10 community of practice of a person is taken here to be the closest Topic rated not interesting=-10 group of people, based on specific features they have in common with that given person. A community of practice is thus Figure 4. Profiling algorithm informal group of people who share some common interest in a particular practice [7[27]. Workplace communities of practice An is-a hierarchy of research paper topics is held so that super- mprove organisational performance by maintaining implicit knowledge, helping the spread of new ideas and sol class relationships can be used to infer broader topic interest. as a focus for innovation and driving organisational strategy When a specific topic is browsed, fractional interest is inferred for ach super-class of that topic, using a 1/ eel weighting where Identifying communities of practice is an essential first step to level'refers to how many classes up the is-a tree the super-class understand the knowledge resources of an organization [28] is from the original gure ows a section from the Organisations can bring the right people together to help the identified communities of practice to flourish and expand, for example by providing them with appropriate infrastructure and give them support and recognition. However, community of practice identification is currently a resource-heavy processInitialise all values of D to 1/N Do for t=1..T call weak-learn(Dt ) calculate error et calculate βt = et /(1-et ) calculate Dt+1 Dt class weight distribution on iteration t N number of classes T number of iterations weak-learn(Dt ) weak learner with distribution Dt et weak_learn error on iteration t βt error adjustment value on iteration t classifier final boosted classifier C all classes classifier = argmax Σ log t = all iterations with result class c c ∈ C βt 1 __ Initialise all values of D to 1/N Do for t=1..T call weak-learn(Dt ) calculate error et calculate βt = et /(1-et ) calculate Dt+1 Dt class weight distribution on iteration t N number of classes T number of iterations weak-learn(Dt ) weak learner with distribution Dt et weak_learn error on iteration t βt error adjustment value on iteration t classifier final boosted classifier C all classes classifier = argmax Σ log t = all iterations with result class c c ∈ C βt 1 __ classifier = argmax Σ log t = all iterations with result class c c ∈ C βt 1 __ Figure 3. AdaBoostM1 boosting algorithm AdaBoostM1 has been shown to improve [11] the performance of weak learner algorithms, particularly for stronger learning algorithms like k-Nearest Neighbour. It is thus a sensible choice to boost our IBk classifier. 5.2 User profiling algorithm The profiling algorithm performs correlation between paper topic classifications and user browsing logs. Whenever a research paper is browsed that has been classified as belonging to a topic, it accumulates an interest score for that topic. Explicit feedback on recommendations also accumulates interest value for topics. The current interest of a topic is computed using the inverse time weighting algorithm shown in Figure 4. ˇ n 1..no of instances Topic interest = Interest value(n) / days old(n) Interest values Paper browsed = 1 Recommendation followed = 2 Topic rated interesting = 10 Topic rated not interesting = -10 ˇ n 1..no of instances Topic interest = Interest value(n) / days old(n) Interest values Paper browsed = 1 Recommendation followed = 2 Topic rated interesting = 10 Topic rated not interesting = -10 Figure 4. Profiling algorithm An is-a hierarchy of research paper topics is held so that super￾class relationships can be used to infer broader topic interest. When a specific topic is browsed, fractional interest is inferred for each super-class of that topic, using a 1/2level weighting where ‘level’ refers to how many classes up the is-a tree the super-class is from the original topic. Figure 5 shows a section from the research paper topic ontology. Artificial Intelligence Hypermedia E-Commerce Interface Agents Mobile Agents Multi-Agent-Systems Recommender Systems Agents Belief Networks Fuzzy Game Theory Genetic Algorithms Genetic Programming Knowledge Representation Information Filtering Information Retrieval Machine Learning Natural Language Neural Networks Philosophy [AI] Robotics [AI] Speech [AI] Vision [AI] Text Classification Ontologies Adaptive Hypermedia Hypertext Design Industrial Hypermedia Literature [hypermedia] Open Hypermedia Spatial Hypertext Taxonomic Hypertext Visualization [hypertext] Web [hypermedia] Content-Based Navigation Architecture [open hypermedia] Figure 5. Section of the research paper topic ontology 5.3 Recommendation algorithm Recommendations are formulated from a correlation between the users current topics of interest and papers classified as belonging to those topics. A paper is only recommended if it does not appear in the users browsed URL log, ensuring that recommendations have not been seen before. For each user, the top three interesting topics are selected with 10 recommendations made in total. Papers are ranked in order of the recommendation confidence before being presented to the user. Figure 6 shows the recommendation algorithm. Recommendation confidence = classification confidence * topic interest value Figure 6. Recommendation algorithm 6. ONTOCOPI The Ontology-based Communities of Practice Identifier (OntoCoPI) [2] is an experimental system that uses the AKT ontology to help identifying communities of practice (CoP). The community of practice of a person is taken here to be the closest group of people, based on specific features they have in common with that given person. A community of practice is thus an informal group of people who share some common interest in a particular practice [7] [27]. Workplace communities of practice improve organisational performance by maintaining implicit knowledge, helping the spread of new ideas and solutions, acting as a focus for innovation and driving organisational strategy. Identifying communities of practice is an essential first step to understand the knowledge resources of an organization [28]. Organisations can bring the right people together to help the identified communities of practice to flourish and expand, for example by providing them with appropriate infrastructure and give them support and recognition. However, community of practice identification is currently a resource-heavy process