Sun, 06 Jul 2008 02:09:28 BST CiteULike: suleehs's library [201 articles] http://www.citeulike.org/user/suleehs CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/suleehs/article/1718703 <i>ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 1-4.</i> Introduction to recommender systems: Algorithms and Evaluation Joseph Konstan doi:10.1145/963770.963771 ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 1-4. 2007-10-02T08:07:25-00:00 2004 ACM Trans. Inf. Syst. 1046-8188 22 1 1 4 ACM Press algorithm recommender http://www.citeulike.org/user/suleehs/article/2570923 <i>(2002)</i><br /><br />Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user's perspective. The goal of our research is to study users' interactions with recommender systems in order to develop general design guidelines. We have studied users' interactions with 11 online recommender systems. Our studies have... Interaction design for recommender systems K Swearingen S Rashmi (2002) 2008-03-22T02:00:34-00:00 2002 interaction recommender http://www.citeulike.org/user/suleehs/article/1140119 <i>(8 Mar 2002)</i><br /><br />Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list. Capturing Knowledge of User Preferences: ontologies on recommender systems SE Middleton DC De Roure NR Shadbolt (8 Mar 2002) 2007-03-04T18:42:59-00:00 2002 middleton recommendation http://www.citeulike.org/user/suleehs/article/2831550 <i>Organizational Behavior and Human Decision Processes, Vol. 93, No. 1. (January 2004), pp. 1-13.</i><br /><br />Seeking advice is a basic practice in making real life decisions. Until recently, however, little attention has been given to it in either empirical studies or theories of decision making. The studies reported here investigate the influence of advice on judgment and the consequences of advice use for judgment accuracy. Respondents were asked to provide final judgments on the basis of their initial opinions and advice presented to them. The respondents' weighting policies were inferred. Analysis of the these policies show that (a) the respondents tended to place a higher weight on their own opinion than on the advisor's opinion (the self/other effect); (b) more knowledgeable individuals discounted the advice more; (c) the weight of advice decreased as its distance from the initial opinion increased; and (d) the use of advice improved accuracy significantly, though not optimally. A theoretical framework is introduced which draws in part on insights from the study of attitude change to explain the influence of advice. Finally the usefulness of advice for improving judgment accuracy is considered. Receiving other people's advice: Influence and benefit Ilan Yaniv doi:10.1016/j.obhdp.2003.08.002 Organizational Behavior and Human Decision Processes, Vol. 93, No. 1. (January 2004), pp. 1-13. 2008-05-25T19:39:44-00:00 2004 Organizational Behavior and Human Decision Processes 93 1 1 13 recommendation trust http://www.citeulike.org/user/suleehs/article/2831409 <i>People and Computers XIX — The Bigger Picture (2006), pp. 251-265.</i><br /><br />Recommender Systems have been developed to help people make choices, for instance when deciding what books to buy or movies to see. Research to date has focused on developing algorithms to improve the predictive accuracy of recommender systems. This paper presents an HCI approach to recommender systems design, based on the strategies people employ when seeking advice in taste domains from various sources. The results from a qualitative study with 44 participants show that participants have different requirements for different choice domains. In taste domains, the relationship between the advice seeker and recommender is extremely important, so ways of indicating social closeness and taste overlap are required. Recommender systems must establish a connection between the advice seeker and recommenders through explanation interfaces and communication functions. “I thought it was terrible and everyone else loved it” — A New Perspective for Effective Recommender System Design Philip Bonhard Angela Sasse doi:10.1007/1-84628-249-7_16 People and Computers XIX — The Bigger Picture (2006), pp. 251-265. 2008-05-25T19:31:11-00:00 2006 People and Computers XIX — The Bigger Picture 251 265 recommendation topic1 http://www.citeulike.org/user/suleehs/article/1457844 <i>(2007), pp. 442-451.</i> Effects of structure and interaction style on distinct search tasks Robert Capra Gary Marchionini Jung Oh Fred Stutzman Yan Zhang doi:10.1145/1255175.1255267 (2007), pp. 442-451. 2007-07-15T17:13:13-00:00 2007 442 451 ACM Press pbrecommendation search http://www.citeulike.org/user/suleehs/article/710692 <i>(2006), pp. 1057-1066.</i> Accounting for taste: using profile similarity to improve recommender systems Philip Bonhard Clare Harries John Mccarthy Angela Sasse doi:10.1145/1124772.1124930< (2006), pp. 1057-1066. 2006-06-26T04:44:38-00:00 2006 1057 1066 ACM Press explanation pbpaws recommender trust http://www.citeulike.org/user/suleehs/article/2827173 <i>(2001)</i><br /><br />The quality of recommendations and usability of six online recommender systems (RS) was examined. Three book RS (Amazon.com, RatingZone & Sleeper) and three movie RS (Amazon.com, MovieCritic, Reel.com) were evaluated. Quality of recommendations was explored by comparing recommendations made by RS to recommendations made by the user's friends. Results showed that the user's friends consistently provided better recommendations than RS. However, users did find items recommended by online RS... Comparing Recommendations Made by Online Systems and Friends Rashmi Sinha Kirsten Swearingen (2001) 2008-05-24T00:06:41-00:00 2001 pbpaws recommendation topic1 trust http://www.citeulike.org/user/suleehs/article/1738250 <i>User Modeling 2007 (2007), pp. 278-287.</i><br /><br />Recommender systems produce social networks as a side effect of predicting what users will like. However, the potential for these social networks to aid in recommending items is largely ignored. We propose a recommender system that works directly with these networks to distribute and recommend items: the informal exchange of information (word of mouth communication) is supported rather than replaced. The paper describes the push-poll approach and evaluates its performance at predicting user ratings for movies against a collaborative filtering algorithm. Overall, the push-poll approach performs significantly better while being computationally efficient and suitable for dynamic domains (e.g. recommending items from RSS feeds). Push-Poll Recommender System: Supporting Word of Mouth Andrew Webster Julita Vassileva doi:10.1007/978-3-540-73078-1_31 User Modeling 2007 (2007), pp. 278-287. 2007-10-08T02:12:24-00:00 2007 User Modeling 2007 278 287 topic1 trust http://www.citeulike.org/user/suleehs/article/394239 <i>Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 103-108.</i> Introduction to the Special Issue on Explanation in Case-Based Reasoning David Leake David Mcsherry doi:10.1007/s10462-005-4606-8 Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 103-108. 2005-11-15T16:46:11-00:00 2005 Artificial Intelligence Review 0269-2821 24 2 103 108 Kluwer Academic Publishers explanation http://www.citeulike.org/user/suleehs/article/2611574 <i>(2006), pp. 1050-1057.</i> Content-based music filtering system with editable user profile Yoshinori Hijikata Kazuhiro Iwahama Shogo Nishida doi:10.1145/1141277.1141526 (2006), pp. 1050-1057. 2008-03-30T02:56:10-00:00 2006 1050 1057 ACM contentbased recommendation http://www.citeulike.org/user/suleehs/article/100364 <i>(2004), pp. 109-116.</i> The challenge of information visualization evaluation Catherine Plaisant doi:10.1145/989863.989880 (2004), pp. 109-116. 2005-02-22T22:19:19-00:00 2004 109 116 ACM Press explanation visualization http://www.citeulike.org/user/suleehs/article/232152 <i></i><br /><br />This digital document is an article from Journal of Interactive Learning Research, published by Association for the Advancement of Computing in Education (AACE) on March 22, 2003. The length of the article is 6220 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available in your Amazon.com Digital Locker immediately after purchase. You can view it with any web browser.<BR><BR><strong>Citation Details</strong><br><strong>Title:</strong> Supporting "word-of-mouth" social networks through collaborative information filtering.(Altered Vista)<br><strong>Author:</strong> Mimi M. Recker<br><strong>Publication:</strong> <em>Journal of Interactive Learning Research</em> (Refereed)<br><strong>Date:</strong> March 22, 2003<br><strong>Publisher:</strong> Association for the Advancement of Computing in Education (AACE)<br><strong>Volume:</strong> 14 <strong>Issue:</strong> 1 <strong>Page:</strong> 79(20)<BR><BR>Distributed by Thomson Gale Supporting "word-of-mouth" social networks through collaborative information filtering.(Altered Vista) : An article from: Journal of Interactive Learning Research Mimi Recker Andrew Walker 2005-06-20T01:23:12-00:00 informationpropagation http://www.citeulike.org/user/suleehs/article/2604964 <i>Inf. Process. Manage., Vol. 40, No. 4. (May 2004), pp. 655-675.</i> WiIRE: the web interactive information retrieval experimentation system prototype Elaine Toms Luanne Freund Cara Li doi:10.1016/j.ipm.2003.08.006 Inf. Process. Manage., Vol. 40, No. 4. (May 2004), pp. 655-675. 2008-03-28T04:45:49-00:00 2004 Inf. Process. Manage. 0306-4573 40 4 655 675 Pergamon Press, Inc. informativeness measurement http://www.citeulike.org/user/suleehs/article/2604958 <i>(1992), pp. 23-36.</i> Measuring the informativeness of a retrieval process Jean Tague-Sutcliffe doi:10.1145/133160.133171 (1992), pp. 23-36. 2008-03-28T04:43:03-00:00 1992 23 36 ACM informativeness measurement http://www.citeulike.org/user/suleehs/article/2570521 <i>(1998), pp. 368-379.</i> On the Discovery of Interesting Patterns in Association Rules Sridhar Ramaswamy Sameer Mahajan Abraham Silberschatz (1998), pp. 368-379. 2008-03-21T18:45:21-00:00 1998 368 379 Morgan Kaufmann Publishers Inc. apriori datamining http://www.citeulike.org/user/suleehs/article/4112 <i>(December--JanuaryMay~ 1994), pp. 487-499.</i><br /><br />We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Experiments with synthetic as well as real-life data show that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems. We also show how the best features of the two proposed ... Fast Algorithms for Mining Association Rules Rakesh Agrawal Ramakrishnan Srikant (December--JanuaryMay~ 1994), pp. 487-499. 2004-12-17T03:46:55-00:00 1994 487 499 Morgan Kaufmann apriori datamining http://www.citeulike.org/user/suleehs/article/2570519 <i>(2001), pp. 71-82.</i> Enhancing the Apriori Algorithm for Frequent Set Counting Salvatore Orlando Paolo Palmerini Raffaele Perego (2001), pp. 71-82. 2008-03-21T18:42:48-00:00 2001 71 82 Springer-Verlag apriori datamining http://www.citeulike.org/user/suleehs/article/2570518 <i>pp. 329-342.</i><br /><br />A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies. A personalized recommender system based on web usage mining and decision tree induction YH Cho pp. 329-342. 2008-03-21T18:41:50-00:00 329 342 datamining recommendation http://www.citeulike.org/user/suleehs/article/688050 <i>Data Mining and Knowledge Discovery, Vol. 6, No. 1. (January 2002), pp. 83-105.</i> Efficient Adaptive-Support Association Rule Mining for Recommender Systems Weiyang Lin Sergio Alvarez Carolina Ruiz doi:10.1023/A:1013284820704 Data Mining and Knowledge Discovery, Vol. 6, No. 1. (January 2002), pp. 83-105. 2006-06-07T08:25:49-00:00 2002 Data Mining and Knowledge Discovery 6 1 83 105 datamining recommendation http://www.citeulike.org/user/suleehs/article/2570508 <i>(JanuaryApril--JanuaryJuly~ 1997), pp. 67-73.</i><br /><br />The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a specific item or rules that contain children of a specific item in a hierarchy. While such constraints can be applied as a postprocessing step, integrating them into the mining algorithm can dramatically... Mining Association Rules with Item Constraints Ramakrishnan Srikant Quoc Vu Rakesh Agrawal (JanuaryApril--JanuaryJuly~ 1997), pp. 67-73. 2008-03-21T18:36:20-00:00 1997 67 73 AAAI Press datamining http://www.citeulike.org/user/suleehs/article/2570503 <i>Adaptive Hypermedia and Adaptive Web-Based Systems (2004), pp. 176-184.</i><br /><br />Conversational recommender systems help to guide users through a product-space towards a particular product that meets their specific requirements. During the course of a “conversation” with the user the recommender system will suggest certain products and use feedback from the user to refine future suggestions. Critiquing has proven to be a powerful and popular form of feedback. Critiques allow the user to express a preference over part of the feature-space; for example, in a vacation/travel recommender a user might indicate that they are looking for a “less expensive” vacation than the one suggested, thereby critiquing the price feature. Usually the set of critiques that the user can chose from is fixed as part of the basic recommender interface. In this paper we will propose a more dynamic critiquing approach where high-quality critiques are automatically generated during each recommendation cycle from the remaining product-cases. We show that these dynamic critiques can lead to more efficient recommendation performance by helping the user to more rapidly focus in on the right region of the product-space. On the Dynamic Generation of Compound Critiques in Conversational Recommender Systems Kevin Mccarthy James Reilly Lorraine Mcginty Barry Smyth Adaptive Hypermedia and Adaptive Web-Based Systems (2004), pp. 176-184. 2008-03-21T18:35:10-00:00 2004 Adaptive Hypermedia and Adaptive Web-Based Systems 176 184 example-critique explanation http://www.citeulike.org/user/suleehs/article/2570417 <i>Social Networks, Vol. 29, No. 3. (July 2007), pp. 349-356.</i> The network is personal: Introduction to a special issue of Social Networks Barry Wellman doi:10.1016/j.socnet.2007.01.006 Social Networks, Vol. 29, No. 3. (July 2007), pp. 349-356. 2008-03-21T17:27:53-00:00 2007 Social Networks 29 3 349 356 socialnetwork topic1 http://www.citeulike.org/user/suleehs/article/904434 <i>Social Networks, Vol. 21, No. 2. (April 1999), pp. 111-130.</i><br /><br />This paper evaluates the reliability and validity of network measurement instruments for measuring social support. The authors present and discuss the results from eight experiments which were designed to analyze the quality of four measurement scales: (1) binary, (2) categorical, (3) categorical with labels, and (4) line production, as well as two measurement techniques for listing alters (free recall and recognition). Reliability and validity were estimated by the true score multitrait-multimethod (MTMM) approach. Meta-analysis of factors affecting the reliability and the validity of network measurement was done by multiple classification analysis (MCA). The results show that the binary scale and the first presentation of measurement instruments are the least reliable. Surprisingly, the two data collection techniques (free recall and recognition) yield equally reliable data. Evaluation of social network measurement instruments Anuska Ferligoj Valentina Hlebec doi:10.1016/S0378-8733(99)00007-6 Social Networks, Vol. 21, No. 2. (April 1999), pp. 111-130. 2006-10-19T05:08:22-00:00 1999 Social Networks 21 2 111 130 socialnetwork http://www.citeulike.org/user/suleehs/article/2497575 <i>Semantics, Web and Mining (2006), pp. 180-195.</i><br /><br />Web Usage Mining (WUM) is the application of data mining techniques over web server logs in order to extract navigation usage patterns. Identifying the relevant and interesting patterns, and to understand what knowledge they represent in the domain is the goal of the Pattern Analysis phase, one of the phases of the WUM process. Pattern analysis is a critical phase in WUM due to two main reasons: a) mining algorithms yield a huge number of patterns; b) there is a significant semantic gap between URLs and events performed by users. In this paper, we discuss an ontology-based approach to support the analysis of sequential navigation patterns, discussing the main features of the O3R (Ontology-based Rules Retrieval and Rummaging) prototype. O3R functionality is targeted at supporting the comprehension of patterns through interactive pattern rummaging, as well as on the identification of potentially interesting ones. All functionality is based on the availability of the domain ontology, which dynamically provides meaning to URLs. The paper provides an overall view of O3R, details the rummaging functionality, and discusses preliminary results on the use of O3R. Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns Mariângela Vanzin Karin Becker doi:10.1007/11908678_12 Semantics, Web and Mining (2006), pp. 180-195. 2008-03-09T23:02:55-00:00 2006 Semantics, Web and Mining 180 195 com dm http://www.citeulike.org/user/suleehs/article/2497571 <i>Semantics, Web and Mining (2006), pp. 147-162.</i><br /><br />Web personalization is the process of customizing a web site to the needs of each specific user or set of users. Personalization of a web site may be performed by the provision of recommendations to the users, high-lighting/adding links, creation of index pages, etc. The web personalization systems are mainly based on the exploitation of the navigational patterns of the web site’s visitors. When a personalization system relies solely on usage-based results, however, valuable information conceptually related to what is finally recommended may be missed. The exploitation of the web pages’ semantics can considerably improve the results of web usage mining and personalization, since it provides a more abstract yet uniform and both machine and human understandable way of processing and analyzing the usage data. The underlying idea is to integrate usage data with content semantics, expressed in ontology terms, in order to produce semantically enhanced navigational patterns that can subsequently be used for producing valuable recommendations. In this paper we propose a semantic web personalization system, focusing on word sense disambiguation techniques which can be applied in order to semantically annotate the web site’s content. Introducing Semantics in Web Personalization: The Role of Ontologies Magdalini Eirinaki Dimitrios Mavroeidis George Tsatsaronis Michalis Vazirgiannis doi:10.1007/11908678_10 Semantics, Web and Mining (2006), pp. 147-162. 2008-03-09T23:02:25-00:00 2006 Semantics, Web and Mining 147 162 com dm http://www.citeulike.org/user/suleehs/article/2439238 <i>Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 76-83.</i><br /><br />Web2.0 has enabled contributions to the Web on an unprecedented scale, through simple interfaces that provide engaging interactions. This wealth of data has spawned countless mashups that integrate heterogenous information, but using techniques that will not scale beyond a handful of sources. In contrast, the Semantic Web provides the key to large-scale data integration, yet still lacks approachable interfaces allowing contributions from non-specialists. In this paper we present Revyu, a reviewing and rating site in the Web2.0 mould that is built on Semantic Web infrastructure and both publishes and consumes linked RDF data. This combination of approaches affords ease of interaction for regular users and ease of integration with external data sources. Ease of interaction plus ease of integration: Combining Web2.0 and the Semantic Web in a reviewing site Tom Heath Enrico Motta doi:10.1016/j.websem.2007.11.009 Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 76-83. 2008-02-28T00:52:51-00:00 2008 Web Semantics: Science, Services and Agents on the World Wide Web 6 1 76 83 dlpaws ontology semanticweb http://www.citeulike.org/user/suleehs/article/2439236 <i>Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 70-75.</i><br /><br />A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other's strengths. We believe that future Web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities. The two cultures: Mashing up Web 2.0 and the Semantic Web Anupriya Ankolekar Markus Krotzsch Thanh Tran Denny Vrandecic doi:10.1016/j.websem.2007.11.005 Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 70-75. 2008-02-28T00:51:52-00:00 2008 Web Semantics: Science, Services and Agents on the World Wide Web 6 1 70 75 dlpaws ontology semanticweb http://www.citeulike.org/user/suleehs/article/2273576 <i>Web Semant.</i><br /><br />Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples. © 2007 Elsevier B.V. All rights reserved. Article in Press Discovering shared conceptualizations in folksonomies R Ja?schke A Hotho C Schmitz B Ganter G Stumme doi:10.1016/j.websem.2007.11.004 Web Semant. 2008-01-22T12:43:24-00:00 Web Semant. dlpaws ontology semanticweb http://www.citeulike.org/user/suleehs/article/2439230 <i>Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 21-28.</i><br /><br />Large volumes of content (bookmarks, reviews, videos, etc.) are currently being created on the "Social Web", i.e. on Web 2.0 community sites, and this content is being annotated and commented upon. The ability to view an individual's entire contribution to the Social Web would be an interesting and valuable service, particularly important as social networks are often being formed through created content and things that people have in common ("object-centred sociality"). SIOC is a Semantic Web research project that aims to describe online communities on the Social Web. This paper describes how SIOC and the Semantic Web can enable linking and reuse scenarios of data from Web 2.0 community sites, and introduces a SIOC Types module to further specify the type of content items and act as a "glue" between user posts and the content items created and annotated by users. Using the Semantic Web for linking and reusing data across Web 2.0 communities U Bojars JG Breslin A Finn S Decker doi:10.1016/j.websem.2007.11.010 Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 21-28. 2008-02-28T00:49:38-00:00 2008 Web Semantics: Science, Services and Agents on the World Wide Web 6 1 21 28 dlpaws ontology semanticweb http://www.citeulike.org/user/suleehs/article/2439228 <i>Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 4-13.</i><br /><br />Summary What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the "collective intelligence" of the Social Web with knowledge representation and reasoning techniques of the Semantic Web. Collective knowledge systems: Where the Social Web meets the Semantic Web Tom Gruber doi:10.1016/j.websem.2007.11.011 Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 6, No. 1. (February 2008), pp. 4-13. 2008-02-28T00:48:47-00:00 2008 Web Semantics: Science, Services and Agents on the World Wide Web 6 1 4 13 dlpaws ontology socialweb http://www.citeulike.org/user/suleehs/article/2347604 <i></i><br /><br />. The traditional field of Machine Learning is concerned with techniques for modifying the behavior of a computer agent over time in order to improve its usefulness to people. This problem has traditionally been formulated as an abstract mathematical problem of inducing a generalized function from assertions representing the "perceptions" or "experience" of the agent. This paper argues that this formulation of the Machine Learning problem is unnecessarily restrictive, and leaves out... Interaction Is The Key To Machine Learning Applications Henry Lieberman 2008-02-07T02:29:58-00:00 machinelearning http://www.citeulike.org/user/suleehs/article/2389949 <i>Case-Based Reasoning Research and Development (2007), pp. 329-343.</i><br /><br />This paper describes and evaluates a case-based approach to personalizing Web search by post-processing the results returned by a Web search engine to reflect the interests of a community of like-minded searchers. The search experiences of a community of users are captured as a case base of textual cases, which serves as a way to bias future search results in line with community interests. Enhancing Case-Based, Collaborative Web Search Oisín Boydell Barry Smyth doi:10.1007/978-3-540-74141-1_23 Case-Based Reasoning Research and Development (2007), pp. 329-343. 2008-02-17T03:33:02-00:00 2007 Case-Based Reasoning Research and Development 329 343 collaborativefiltering recommendation http://www.citeulike.org/user/suleehs/article/894279 <i>(2004), pp. 393-402.</i> Shilling recommender systems for fun and profit Shyong Lam John Riedl doi:10.1145/988672.988726 (2004), pp. 393-402. 2006-10-12T14:28:42-00:00 2004 393 402 ACM Press collaborativefiltering dlpaws recommendation http://www.citeulike.org/user/suleehs/article/2431996 <i></i><br /><br />Collaborative recommendation has emerged as an effective technique for a personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To shed light on this question, we analyze the robustness of collaborative filtering: the ability to make recommendations despite (possibly intentional) noisy product ratings. We formalize robustness in machine learning terms, develop two theoretical justified models of... Robustness Analyses of Instance-Based Collaborative Recommendation Nicholas Kushmerick 2008-02-27T01:06:35-00:00 collaborativefiltering dlpaws recommendation trust http://www.citeulike.org/user/suleehs/article/2431995 <i>Database and Expert Systems Applications (2002), pp. 213-241.</i><br /><br />The growth and popularity of Internet applications has reinforced the need for effective information filtering techniques. The collaborative filtering approach is now a popular choice and has been implemented in many on-line systems. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one important performance measure has been omitted from the research to date -that is the robustness of the algorithm. In essence, robustness measures the power of the algorithm to make good predictions in the presence of noisy data. In this paper, we argue that robustness is an important system characteristic, and that it must be considered from the point-of-view of potential attacks that could be made on a system by malicious users. We propose a definition for system robustness, and identify system characteristics that influence robustness. Several attack strategies are described in detail, and experimental results are presented for the scenarios outlined. Promoting Recommendations: An Attack on Collaborative Filtering Michael O’mahony Neil Hurley Guenole Silvestre doi:10.1007/3-540-46146-9_49 Database and Expert Systems Applications (2002), pp. 213-241. 2008-02-27T01:05:32-00:00 2002 Database and Expert Systems Applications 213 241 collaborativefiltering dlpaws recommendation trust http://www.citeulike.org/user/suleehs/article/2431987 <i>ACM Trans. Inter. Tech., Vol. 4, No. 4. (November 2004), pp. 344-377.</i> Collaborative recommendation: A robustness analysis Michael O'Mahony Neil Hurley Nicholas Kushmerick Guénolé Silvestre doi:10.1145/1031114.1031116 ACM Trans. Inter. Tech., Vol. 4, No. 4. (November 2004), pp. 344-377. 2008-02-27T01:00:13-00:00 2004 ACM Trans. Inter. Tech. 1533-5399 4 4 344 377 ACM collaborativefiltering dlpaws recommendation trust http://www.citeulike.org/user/suleehs/article/2431676 <i>(2002)</i><br /><br />Trust is one of the most important social concepts that helps human agents to cope with their social environment and is present in all human interaction. Like in real world, agents should rely in some agents and mistrust in other ones to achieve a purpose. In this paper we develop a model of trust in the collaborative world as a new approach of recommender agents development. Mainly, we provide recommender agents with a technology to look for similar agents that advice him. The model presented... Developing Trust in Recommender Agents M Montaner B Lpez J de La (2002) 2008-02-26T23:55:10-00:00 2002 dlpaws recommendation trust http://www.citeulike.org/user/suleehs/article/2431464 <i>(2004)</i><br /><br />While most research on the topic of trust on the semantic web has focused largely on digital signatures, certificates, and authentication, more social notions of trust which are reputation-based are starting to gain attention. In this paper, we describe an algorithm for generating locally-calculated reputation ratings from a Semantic Web Social Network. We present mathematical and experimental results that show the effectiveness of this algorithm to accurately infer the reputation of a... Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-based Social Networks J Golbeck J Hendler (2004) 2008-02-26T21:47:26-00:00 2004 dlpaws trust http://www.citeulike.org/user/suleehs/article/2430503 <i>Technovation, Vol. 26, No. 12. (December 2006), pp. 1357-1365.</i><br /><br />This paper is the first of the series of studies entitled "Innovativeness and Involvement as Determinants of Website Loyalty", which was designed to test Foxall's [1995. Cognitive styles of consumer initiators. Technovation 15(5), 269-288] style/involvement model in the context of Internet buyer behaviours. The study was built on 1044 Taiwan Internet buyers randomly selected from a well-known brand's Website in the printer market. Foxall (1995) proposed that consumer choice is shaped by adaptive-innovative cognitive style [Kirton, 1976. Adaptors and innovators--a description and measure. Journal of Applied Psychology 61(5), 622-629] and product-domain involvement [Zaichkowsky, 1985. Measuring the involvement construct. Journal of Consumer Research 12(3), 341-352]. Aiming to provide more theoretical insights of the style/involvement model, Goldsmith and Hofacker's [1991. Measuring consumer innovativeness. Journal of the Academy of Marketing Science 19(3), 209-221] Domain Specific Innovativeness (DSI) scale and Mittal's [1989. Measuring purchase-decision involvement. Psychology and Marketing 6(2), 147-162] Purchase Decision Involvement (PDI) scale were used in order to examine Foxall's model in a more domain specific context in contrast to the more abstract measurements previously employed. The sample was divided into four consumer segments according to their DSI and PDI scores. Findings revealed that these four segments differed significantly in their brand loyalty in the traditional market, perceived risk when buying at the brand's Website, attitudinal loyalty to the brand's Website, and behavioural loyalty to the brand's Website (actual buying frequency at the brand's Website). Discussions of how consumers' cognitive style and involvement level interact with each other and impact on their decision-making process underlying the tendency to brand loyalty, perceived risk and Website loyalty were presented. Innovativeness and Involvement as Determinants of Website Loyalty: I. A test of the style/involvement model in the context of Internet buying Hui-Chih Wang John Pallister Gordon Foxall doi:10.1016/j.technovation.2005.11.004 Technovation, Vol. 26, No. 12. (December 2006), pp. 1357-1365. 2008-02-26T18:08:15-00:00 2006 Technovation 26 12 1357 1365 dlpaws involvement trust http://www.citeulike.org/user/suleehs/article/2430488 <i>(1998), pp. 180-194.</i><br /><br />A random sample of 308 UK consumers was used to compare two scales for the measurement of consumer involvement - Zaichkowsky’s revised Personal Involvement Inventory and Mittal’s Purchase-decision Involvement Scale - in terms of internal reliability, dimensionality, convergent validity, discriminant validity, and criterion validity. In general, both inventories perform well but the results raise interesting questions about the emotional versus rational structure of consumer involvement with financial services. The practical implications of the results for consumer research and the marketing of financial services are discussed. Measuring purchase decision involvement for financial services: comparison of the Zaichkowsky and Mittal scales GR Foxall (1998), pp. 180-194. 2008-02-26T18:06:35-00:00 1998 180 194 dlpaws involvement http://www.citeulike.org/user/suleehs/article/2430473 <i>(2007), pp. 1-8.</i> Private distributed collaborative filtering using estimated concordance measures Neal Lathia Stephen Hailes Licia Capra doi:10.1145/1297231.1297233 (2007), pp. 1-8. 2008-02-26T17:57:02-00:00 2007 1 8 ACM dlpaws recommendation http://www.citeulike.org/user/suleehs/article/2430467 <i>(2007), pp. 129-132.</i> A probabilistic model for item-based recommender systems Ming Li Benjamin Dias Wael El-Deredy Paulo Lisboa doi:10.1145/1297231.1297253 (2007), pp. 129-132. 2008-02-26T17:55:20-00:00 2007 129 132 ACM dlpaws recommendation http://www.citeulike.org/user/suleehs/article/2430455 <i>(2007), pp. 153-156.</i> Effective explanations of recommendations: user-centered design Nava Tintarev Judith Masthoff doi:10.1145/1297231.1297259 (2007), pp. 153-156. 2008-02-26T17:49:59-00:00 2007 153 156 ACM dlpaws explanation http://www.citeulike.org/user/suleehs/article/763598 <i>(2005), pp. 22-32.</i> Improving recommendation lists through topic diversification Cai-Nicolas Ziegler Sean Mcnee Joseph Konstan Georg Lausen doi:10.1145/1060745.1060754 (2005), pp. 22-32. 2006-07-18T22:16:11-00:00 2005 22 32 ACM Press diversity dlpaws recommendation http://www.citeulike.org/user/suleehs/article/2338436 <i>Case-Based Reasoning Research and Development (2003), pp. 1067-1067.</i><br /><br />A common cause of retrieval failure in case-based reasoning (CBR) approaches to product recommendation is that the retrieved cases, usually those that are most similar to the target query, are not sufficiently representative of compromises that the user may be prepared to make. We present a new approach to retrieval in which similarity and compromise play complementary roles, thereby increasing the likelihood that one of the retrieved cases will be acceptable to the user. We also show how the approach can be extended to address the requirements of domains in which the user is not just seeking a single item that closely matches her query, but would like to be informed of all items that are likely to be of interest. Similarity and Compromise David Mcsherry doi:10.1007/3-540-45006-8_24 Case-Based Reasoning Research and Development (2003), pp. 1067-1067. 2008-02-06T03:40:42-00:00 2003 Case-Based Reasoning Research and Development 1067 1067 dlpaws recommendation http://www.citeulike.org/user/suleehs/article/409973 <i>(2005), pp. 175-182.</i> Experiments in dynamic critiquing Kevin Mccarthy James Reilly Lorraine Mcginty Barry Smyth doi:10.1145/1040830.1040871 (2005), pp. 175-182. 2005-11-28T11:06:41-00:00 2005 175 182 ACM Press dlpaws example-critique recommendation http://www.citeulike.org/user/suleehs/article/749877 <i>Int. J. Hum.-Comput. Stud., Vol. 58, No. 6. (June 2003), pp. 783-812.</i> Empirical research in on-line trust: a review and critical assessment Sonja Grabner-Kr&\#228;uter Ewald Kaluscha doi:10.1016/S1071-5819(03)00043-0 Int. J. Hum.-Comput. Stud., Vol. 58, No. 6. (June 2003), pp. 783-812. 2006-07-11T01:51:23-00:00 2003 Int. J. Hum.-Comput. Stud. 1071-5819 58 6 783 812 Academic Press, Inc. dlpaws recommendation trust http://www.citeulike.org/user/suleehs/article/2414975 <i>(2004), pp. 22-29.</i> Designing example-critiquing interaction Boi Faltings Pearl Pu Marc Torrens Paolo Viappiani doi:10.1145/964442.964449 (2004), pp. 22-29. 2008-02-22T18:26:23-00:00 2004 22 29 ACM dlpaws example-critique recommendation http://www.citeulike.org/user/suleehs/article/2414954 <i>(1998), pp. 16-22.</i><br /><br />In this paper, we present a new approach to support the decision of selecting one object out of a set of alternatives. As compared to previous approaches, the distinctive feature of our approach is that neither the user, nor the system need to build a model of user's preferences. Our proposal is to integrate a system for interactive data exploration and analysis with a multimedia explanation facility. The explanation facility supports the user in understanding unexpected aspects of the data.... Multimedia Explanations in IDEA Decision Support System Giuseppe Carenini Johanna Moore (1998), pp. 16-22. 2008-02-22T18:24:30-00:00 1998 16 22 dlpaws explanation recommendation