ALBERT

Description: Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations. Content Type Journal Article Pages 389-409 DOI 10.3233/WIA-140302 Authors Shafiq Alam, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Gillian Dobbie, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Yun Sing Koh, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Patricia Riddle, 38 Princes Street, Auckland 1010, New Zealand. E-mail: {gill,ykoh,pat}@cs.auckland.ac.nz Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training. Content Type Journal Article Pages 375-388 DOI 10.3233/WIA-140301 Authors Vinita Nahar, School of Information Technology and Electrical Engineering, The University of Queensland, Australia. E-mail: v.nahar@uq.edu.au/vinita.nahar.uq@gmail.com, xueli@itee.uq.edu.au Xue Li, School of Information Technology and Electrical Engineering, The University of Queensland, Australia. E-mail: v.nahar@uq.edu.au/vinita.nahar.uq@gmail.com, xueli@itee.uq.edu.au Hao Lan Zhang, NIT, Zhejiang University, Ningbo, Zhejiang Province, China. E-mail: haolan.zhang@nit.zju.edu.cn Chaoyi Pang, NIT, Zhejiang University, Ningbo, Zhejiang Province, China. E-mail: haolan.zhang@nit.zju.edu.cn Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: This special issue particularly focuses on using agent-based methods to solve the complex computational problems arising in Big Data environments. It covers the recent advances in the areas of distributed problem solving, agent-based data mining, as well as recommendation systems, working with data extracted from both physical and online environments. Content Type Journal Article Category Guest editorial Pages 343-345 DOI 10.3233/WIA-140300 Authors Hao Lan Zhang, Center for SCDM, NIT, Zhejiang University, Ningbo City, China. E-mail: haolan.zhang@nit.zju.edu.cn Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore. E-mail: hclau@smu.edu.sg Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at the single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing and interacting agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at the same nodes simultaneously. As all nodes are capacitated, if a particular node receives more agents than its capacity, all agents at that node will be made to wait and agents suffer collectively as a result (in terms of extra time needed for queueing). Due to the decentralized nature of the problem, MOPTCC cannot be solved in a centralized manner; instead, we need to seek out equilibrium solutions; and if this is not possible, at least approximated equilibrium solutions. The major contribution of this paper is the formulation of the problem, and our first attempt in identifying an efficient and effective equilibrium-seeking procedure for MOPTCC. Content Type Journal Article Pages 347-358 DOI 10.3233/WIA-140304 Authors Cen Chen, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Shih-Fen Cheng, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore. E-mail: {cenchen.2012,sfcheng,hclau}@smu.edu.sg Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall. Content Type Journal Article Pages 359-373 DOI 10.3233/WIA-140303 Authors Xun Zhou, UCAS-VU Joint Lab for Social Computing and E-Health Research, University of Chinese Academy of Sciences, Beijing, China. E-mail: zhouxuntthappy@163.com Jing He, Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia. E-mail: {Jing.He,Yanchun.Zhang}@vu.edu.au Guangyan Huang, School of Information Technology, Deakin University, Melbourne, Australia. E-mail: guangyan.huang@deakin.edu.au Yanchun Zhang, Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia. E-mail: {Jing.He,Yanchun.Zhang}@vu.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 4 / 2014

Description: Fervent and agile communication on social networking sites and in virtual organizations provides opportunities for potential issues to trigger individuals into individual action as well as the attraction and mobilization of like-minded individuals into an organization that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring and the diffusion rate of the occupy movement. Previous organizational models lack the representational power to model spontaneous exigencies of a network organization that accounts for rapid rates of dissemination in impromptu networks. This model, therefore, is conceived in a life cycle for a prototypical, emergent networked organization and description of operations therein from formation to dissolution. After describing the life cycle, this article offers insights for a model of a successful emergent organization and an implemented example of a spacecraft organization of satellites. Content Type Journal Article Pages 325-339 DOI 10.3233/WIA-140299 Authors Saad Alqithami, Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States Henry Hexmoor, Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, United States Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: In task environments with large state and action spaces, the use of temporal and state abstraction can potentially improve the decision making performance of agents. However, existing approaches within a reinforcement learning framework typically identify possible subgoal states and instantly learn stochastic subpolicies to reach them from other states. In these circumstances, exploration of the reinforcement learner is unfavorably biased towards local behavior around these subgoals; temporal abstractions are not exploited to reduce required deliberation; and the benefit of employing temporal abstractions is conflated with the benefit of additional learning done to define subpolicies. In this paper, we consider a cognitive agent architecture that allows for the extraction and reuse of temporal abstractions in the form of experience trajectories from a bottom-level reinforcement learning module and a top-level module based on the BDI (Belief-Desire-Intention) model. Here, the reuse of trajectories depends on the situation in which their recording was started. We investigate the efficacy of our approach using two well-known domains – the pursuit and the taxi domains. Detailed simulation experiments demonstrate that the use of experience trajectories as plans acquired at runtime can reduce the amount of decision making without significantly affecting asymptotic performance. The combination of temporal and state abstraction leads to improved performance during the initial learning of the reinforcement learner. Our approach can significantly reduce the number of deliberations required. Content Type Journal Article Pages 267-287 DOI 10.3233/WIA-140296 Authors Jens Pfau, CGI Space, 64295 Darmstadt, Germany. E-mail: jens.pfau@cgi.com Samin Karim, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Michael Kirley, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Liz Sonenberg, Department of Computing and Information Systems, University of Melbourne, 3010, Victoria, Australia. E-mail: {karims,mkirley,l.sonenberg}@unimelb.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: Taste in music is of highly subjective nature, making the recommending of music tracks a challenging research task. With TRECS, our live prototype system, we present a weighted hybrid recommender approach that amalgamates three diverse recommender techniques into one comprehensive score. Moreover, our prototype system peppers the generated result list by some simple serendipity heuristic. This way, users can benefit from recommendations aligned with their current taste in music while gaining some exploratory diversification. Empirical evaluations of the live TRECS system, based on an online evaluation, assess the overall recommendation quality as well as the impact of each of the three sub-recommenders. In addition, to better understand the nature and impact of serendipity in isolation, we conducted another study with another recommender prototype of ours, named SONG STUMBLER. The latter assesses three different serendipity metrics in an online evaluation. Content Type Journal Article Pages 235-248 DOI 10.3233/WIA-140294 Authors Cai-Nicolas Ziegler, XING EVENTS GmbH, Sandstraße 33, D-80335 München, Germany Thomas Hornung, REWE Information Systems GmbH, Humboldtstraße 140-144, D-51149 Köln, Germany. E-mail: thomas.hornung@rewe-group.com Martin Przyjaciel-Zablocki, Dept. of Computer Science, University of Freiburg, Georges-Köhler-Allee 51, D-79110 Freiburg, Germany. E-mail: {zablocki,gausss,lausen}@informatik.uni-freiburg.de Sven Gauß, Dept. of Computer Science, University of Freiburg, Georges-Köhler-Allee 51, D-79110 Freiburg, Germany. E-mail: {zablocki,gausss,lausen}@informatik.uni-freiburg.de Georg Lausen, REWE Information Systems GmbH, Humboldtstraße 140-144, D-51149 Köln, Germany. E-mail: thomas.hornung@rewe-group.com Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy. Content Type Journal Article Pages 289-307 DOI 10.3233/WIA-140297 Authors Yongli Ren, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Gang Li, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Wanlei Zhou, School of Information Technology, Deakin University, 221 Burwood Highway, Vic 3125, Australia. E-mail: yongli@deakin.edu.au, wanlei@deakin.edu.au Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014

Description: We present an agent architecture and a hybrid behavior learning method for it that allows the use of communicated intentions of other agents to create agents that are able to cooperate with various configurations of other agents in fulfilling a task. Our shout-ahead architecture is based on two rule sets, one making decisions without communicated intentions and one with these intentions, and reinforcement learning is used to determine in a particular situation which set is responsible for the final decision. Evolutionary learning is used to learn these rules. Our application of this approach to learning behaviors for units in a computer game shows that the use of shout-ahead using only communicated intentions in the second rule set substantially improves the quality of the learned behavior compared to agents not using shout-ahead. Also, allowing for additional conditions in the second rule set can either improve the quality or worsen it, based on what type of conditions are used. Content Type Journal Article Pages 309-324 DOI 10.3233/WIA-140298 Authors Sanjeev Paskaradevan, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Jörg Denzinger, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Daniel Wehr, Department of Computer Science, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. E-mail: sanjeev.com@gmail.com, {denzinge,dkwehr}@ucalgary.ca Journal Web Intelligence and Agent Systems Online ISSN 1875-9289 Print ISSN 1570-1263 Journal Volume Volume 12 Journal Issue Volume 12, Number 3 / 2014