Thu, 21 Aug 2008 15:14:32 BST CiteULike: Tag recommendation http://www.citeulike.org/tag/recommendation CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/wolfketter/article/2642994 <i>Expert Systems with Applications, Vol. 22, No. 4. (May 2002), pp. 275-284.</i><br /><br />The prosperity of electronic commerce has changed the traditional trading behaviors and more and more people are willing to conduct Internet shopping. However, the exponentially increasing information provided by the Internet enterprises causes the problem of overloaded information, and this inevitably reduces the customer's satisfaction and loyalty. One way to overcome such a problem is to build personalized recommender systems to retrieve product information that really interests the customers. For products that people may purchase relatively often, such as books and CDs, recommender systems can be built to reason about a customer's personal preferences from his purchasing history and then provide the most appropriate information services to meet his needs. On the other hand, for those commodities a general customer does not buy frequently, for example computers and home theater systems, more appropriate are the kinds of recommender systems able to retrieve optimal products based on the customer's current preferences obtained from the iterative system-customer interactions. This paper presents the above two kinds of recommender systems we have developed for supporting Internet commerce. Experimental results show the promise of our systems. Intelligent agent-based systems for personalized recommendations in Internet commerce Wei-Po Lee Chih-Hung Liu Cheng-Che Lu doi:10.1016/S0957-4174(02)00015-5 Expert Systems with Applications, Vol. 22, No. 4. (May 2002), pp. 275-284. 2008-04-08T21:53:34-00:00 2002 Expert Systems with Applications 22 4 275 284 advocate-agents e-commerce personalization recommendation http://www.citeulike.org/user/wolfketter/article/893482 <i>(1995), pp. 202-209.</i> Pointing the way: active collaborative filtering David Maltz Kate Ehrlich doi:10.1145/223904.223930 (1995), pp. 202-209. 2006-10-11T17:19:40-00:00 1995 202 209 ACM Press/Addison-Wesley Publishing Co. collaborative-filtering profiling recommendation http://www.citeulike.org/user/wilmegape/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 association mining recommendation rules http://www.citeulike.org/user/vme64/article/992162 <i>Lecture Notes in Computer Science : The Semantic Web: Research and Applications (2005), pp. 439-454.</i> Activity Based Metadata for Semantic Desktop Search Paul Chirita Rita Gavriloaie Stefania Ghita Wolfgang Nejdl Raluca Paiu doi:10.1007/11431053_30 Lecture Notes in Computer Science : The Semantic Web: Research and Applications (2005), pp. 439-454. 2006-12-13T11:19:24-00:00 2005 Lecture Notes in Computer Science : The Semantic Web: Research and Applications 439 454 recommendation reputation semanticweb socialnetwork trust http://www.citeulike.org/user/vme64/article/988555 <i>Lecture Notes in Computer Science : Trusting Agents for Trusting Electronic Societies (2005), pp. 200-217.</i> A Protocol for a Distributed Recommender System Josã© Vidal doi:10.1007/11532095_12 Lecture Notes in Computer Science : Trusting Agents for Trusting Electronic Societies (2005), pp. 200-217. 2006-12-11T13:08:55-00:00 2005 Lecture Notes in Computer Science : Trusting Agents for Trusting Electronic Societies 200 217 p2p recommendation reputation socialnetwork system trust http://www.citeulike.org/user/tullachBui/article/177272 <i>Lecture Notes in Computer Science, Vol. 31, No. 55. (2004), pp. 560-572.</i><br /><br />The ranking of offers is an issue in e-commerce that has received a lot of attention in Case-Based Reasoning research. In the absence of a sales assistant, it is important to provide a facility that will bring suitable products and services to the attention of the customer. In this paper we present such a facility that is part of a Personal Travel Assistant (PTA) for booking flights online. The PTA returns a large number of offers (24 on average) and it is important to rank them to bring... Improving Recommendation Ranking by Learning Personal Feature Weights Lorcan Coyle Pádraig Cunningham Lecture Notes in Computer Science, Vol. 31, No. 55. (2004), pp. 560-572. 2005-05-03T11:40:07-00:00 2004 Lecture Notes in Computer Science 31 55 560 572 Springer recommendation similarity http://www.citeulike.org/user/tjpp/article/2859890 <i>EPL (Europhysics Letters), Vol. 82, No. 5. (2008), 58007.</i><br /><br />Recommender systems are significant to help people deal with the world of information explosion and overload. In this letter, we develop a general framework named self-consistent refinement and implement it by embedding two representative recommendation algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods. Information filtering via self-consistent refinement Jie Ren Tao Zhou Yi-Cheng Zhang doi:10.1209/0295-5075/82/58007 EPL (Europhysics Letters), Vol. 82, No. 5. (2008), 58007. 2008-06-03T20:23:30-00:00 2008 EPL (Europhysics Letters) 82 5 58007 networks recommendation http://www.citeulike.org/user/tdrycker/article/2772478 <i>(2005), pp. 625-628.</i> A Scalable Collaborative Filtering Framework Based on Co-Clustering Thomas George Srujana Merugu doi:10.1109/ICDM.2005.14 (2005), pp. 625-628. 2008-05-08T15:27:27-00:00 2005 625 628 IEEE Computer Society personalisation recommendation http://www.citeulike.org/user/tdrycker/article/625550 <i>(2005)</i><br /><br />Rating-based collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. We propose three related slope one schemes with predictors of the form f (x) = x + b, which precompute the average difference between the ratings of one item and another for users who rated both. Slope one algorithms are easy to implement, efficient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates... Slope one predictors for online rating-based collaborative filtering D Lemire A Maclachlan (2005) 2006-05-12T18:52:24-00:00 2005 collaborative-filtering recommendation http://www.citeulike.org/user/tdrycker/article/241012 <i>pp. 43-52.</i><br /><br />Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation... Empirical Analysis of Predictive Algorithms for Collaborative Filtering John Breese David Heckerman Carl Kadie pp. 43-52. 2005-06-30T15:48:40-00:00 43 52 collaborative-filtering performance recommendation http://www.citeulike.org/user/tdrycker/article/2818511 <i>Communications of the ACM, Vol. 40, No. 3. (March 1997)</i><br /><br />this paper we describe the two approaches of content-based and collaborative recommendation, explain how a hybrid system can be created and then describe Fab, an implementation of such a system. We give initial experimental results which show the validity of the assumptions upon which our hybrid approach is based, and we conclude with a brief summary and a description of our ongoing and future work. Combining Content-Based and Collaborative Recommendation Marko Balabanovic Yoav Shoham Communications of the ACM, Vol. 40, No. 3. (March 1997) 2008-05-21T08:24:05-00:00 1997 Communications of the ACM 40 3 personalization recommendation http://www.citeulike.org/user/tdrycker/article/1205686 <i></i><br /><br />In this paper, we present new extensions to traditional approaches to recommender systems by making recommender systems support data warehousing capabilities. In particular, we propose recommender systems to work in multidimensional settings as opposed to the traditional twodimensional user/item environments. We also propose recommender systems to support rich profiling and OLAP capabilities. 1 Extending recommender systems: A multidimensional approach G Adomavicius A Tuzhilin 2007-04-04T09:49:39-00:00 contextw personalisation recommendation http://www.citeulike.org/user/tdpessem/article/1913172 <i>Applications and the Internet Workshops, 2006. SAINT Workshops 2006. International Symposium on (2006), 7 pp..</i><br /><br />In this paper we study the synergy between user behavior, context data, and semantic information in order to enable future services to adapt to different situations based on the recommendations of a service-independent recommender. Therefore, we propose a system that delivers context-aware recommendations, which are based on provided feedback, context data, and an ontology-based content categorization scheme. We provide a detailed overview of the specification, a short description of a possible service scenario and a discussion of the results. Context-aware, ontology-based recommendations C Rack S Arbanowski S Steglich Applications and the Internet Workshops, 2006. SAINT Workshops 2006. International Symposium on (2006), 7 pp.. 2007-11-14T10:18:39-00:00 2006 Applications and the Internet Workshops, 2006. SAINT Workshops 2006. International Symposium on 7 pp. context personalisation recommendation http://www.citeulike.org/user/tdpessem/article/975389 <i>(2000), pp. 241-250.</i> Explaining collaborative filtering recommendations Jonathan Herlocker Joseph Konstan John Riedl doi:10.1145/358916.358995 (2000), pp. 241-250. 2006-12-05T15:51:18-00:00 2000 241 250 ACM Press collaborativefiltering explanations recommendation userinterface http://www.citeulike.org/user/takeha-e/article/782315 <i>Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on (2006), pp. 388-393.</i><br /><br />There have been many attempts to construct web page recommender systems using collaborative filtering. But the domains these systems can cover are very restricted because it is very difficult to assemble user preference data to web pages, and the number of web pages on the Internet is too large. In this paper, we propose the way to construct a new type of web page recommender system covering all over the Internet, by using Folksonomy and Social Bookmark which are getting very popular in these days. Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions S Niwa Takuo Doi S Honiden Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on (2006), pp. 388-393. 2006-08-02T01:26:54-00:00 2006 Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on 388 393 classification folksonomy mining recommendation sbm 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/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/2364719 <i>(2000), pp. 289-296.</i><br /><br />In electronic commerce, a satisfying buyer experience is a key competitive element. We show new techniques for better adapting interaction with an electronic catalog system to actual buying behavior. Our model replaces the sequential separation of needs identification and product brokering with a conversation in which both processes occur simultaneously. This conversation supports the buyer in formulating his or her needs, and in deciding which criteria to apply in selecting a product to buy.... Enriching buyers' experiences: the SmartClient approach Pearl Pu Boi Faltings (2000), pp. 289-296. 2008-02-12T03:29:24-00:00 2000 289 296 dlpaws example-critique recommendation http://www.citeulike.org/user/suleehs/article/2364708 <i></i><br /><br />Recommender systems combine user pro ling and ltering techniques to provide more pro-active and personal information retrieval systems, and have been gaining in popularity as a way of overcoming the ubiquitous information overload problem. Many recommender systems operate as interactive systems that seek feedback from the end-user as part of the recommendation process to revise the user's query. In this paper we examine dierent forms of feedback that have been used in the past and... Comparison-Based Recommendation Lorraine Ginty Barry Smyth 2008-02-12T03:21:21-00:00 dlpaws recommendation http://www.citeulike.org/user/suleehs/article/2364697 <i>(2004), pp. 406-415.</i> Taxonomy-driven computation of product recommendations Cai-Nicolas Ziegler Georg Lausen Lars Schmidt-Thieme doi:10.1145/1031171.1031252 (2004), pp. 406-415. 2008-02-12T03:12:48-00:00 2004 406 415 ACM dlpaws pbpaws recommendation taxonomy 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/595484 <i>(2006), pp. 93-100.</i> Trust building with explanation interfaces Pearl Pu Li Chen doi:10.1145/1111449.1111475 (2006), pp. 93-100. 2006-04-22T19:29:58-00:00 2006 93 100 ACM Press explanation recommendation topic2 toprint 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 http://www.citeulike.org/user/suleehs/article/1633245 <i></i><br /><br />Web recommender systems anticipate the needs of web users and provide them with recommendations to personalize their navigation. Combining Usage, Content, and Structure Data to Improve Web Site Recommendation Jia Li Osmar Zaïane 2007-09-08T05:54:36-00:00 ecommerce recommendation http://www.citeulike.org/user/suleehs/article/2364661 <i>Advances in Case-Based Reasoning (1996), pp. 150-163.</i><br /><br />AI research on case-based reasoning has led to the development of many laboratory case-based systems. As we move towards introducing these systems into work environments, explaining the processes of case-based reasoning is becoming an increasingly important issue. In this paper we describe the notion of a meta-case for illustrating, explaining and justifying case-based reasoning. A meta-case contains a trace of the processing in a problem-solving episode, and provides an explanation of the problem-solving decisions and a (partial) justification for the solution. The language for representing the problem-solving trace depends on the model of problem solving. We describe a task-method-knowledge (TMK) model of problem-solving and describe the representation of meta-cases in the TMK language. We illustrate this explanatory scheme with examples from Interactive Kritik, a computer-based design and learning environment presently under development. Meta-cases: Explaining case-based reasoning Ashok Goel J Murdock doi:10.1007/BFb0020608 Advances in Case-Based Reasoning (1996), pp. 150-163. 2008-02-12T02:46:38-00:00 1996 Advances in Case-Based Reasoning 150 163 explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/394234 <i>Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 199-220.</i> Explaining Compound Critiques James Reilly Kevin Mccarthy Lorraine Mcginty Barry Smyth doi:10.1007/s10462-005-4614-8 Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 199-220. 2005-11-15T16:46:11-00:00 2005 Artificial Intelligence Review 0269-2821 24 2 199 220 Kluwer Academic Publishers explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/394237 <i>Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 145-161.</i> The Explanatory Power of Symbolic Similarity in Case-Based Reasoning Enric Plaza Eva Armengol Santiago Ontanon doi:10.1007/s10462-005-4608-6 Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 145-161. 2005-11-15T16:46:11-00:00 2005 Artificial Intelligence Review 0269-2821 24 2 145 161 Kluwer Academic Publishers explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/394238 <i>Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 109-143.</i> Explanation in Case-Based ReasoningPerspectives and Goals Frode Sormo Jorg Cassens Agnar Aamodt doi:10.1007/s10462-005-4607-7 Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 109-143. 2005-11-15T16:46:11-00:00 2005 Artificial Intelligence Review 0269-2821 24 2 109 143 Kluwer Academic Publishers explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/394236 <i>Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 163-178.</i> A Case-Based Explanation System for Black-Box Systems Conor Nugent Padraig Cunningham doi:10.1007/s10462-005-4609-5 Artificial Intelligence Review, Vol. 24, No. 2. (October 2005), pp. 163-178. 2005-11-15T16:46:11-00:00 2005 Artificial Intelligence Review 0269-2821 24 2 163 178 Kluwer Academic Publishers explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/2343801 <i>Case-Based Reasoning Research and Development (2003), pp. 1065-1065.</i><br /><br />One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on knowledge-intensive approaches to CBR where the cases contain explanation patterns (e.g. SWALE). However, a more knowledge-light approach where the case similarity is the basis for explanation has received little attention. To explore this, we have developed a CBR system for predicting blood-alcohol level. We compare explanations of predictions produced by this system with alternative rule-based explanations. The case-based explanations fare very well in this evaluation and score significantly better than the rule-based alternative. An Evaluation of the Usefulness of Case-Based Explanation Pádraig Cunningham Dónal Doyle John Loughrey doi:10.1007/3-540-45006-8_12 Case-Based Reasoning Research and Development (2003), pp. 1065-1065. 2008-02-06T23:39:33-00:00 2003 Case-Based Reasoning Research and Development 1065 1065 explanation recommendation topic2 toprint http://www.citeulike.org/user/suleehs/article/2392309 <i>(2007), pp. 169-172.</i> The evaluation of a hybrid critiquing system with preference-based recommendations organization Li Chen Pearl Pu doi:10.1145/1297231.1297263 (2007), pp. 169-172. 2008-02-18T03:37:41-00:00 2007 169 172 ACM dlblog dlpaws recommendation http://www.citeulike.org/user/suleehs/article/3096280 <i>Consumer Communications and Networking Conference, 2006. CCNC 2006. 3rd IEEE, Vol. 1 (2006), pp. 282-286.</i> FilmTrust: movie recommendations using trust in web-based social networks J Golbeck J Hendler Consumer Communications and Networking Conference, 2006. CCNC 2006. 3rd IEEE, Vol. 1 (2006), pp. 282-286. 2008-08-07T15:03:34-00:00 2006 Consumer Communications and Networking Conference, 2006. CCNC 2006. 3rd IEEE 1 282 286 recommendation topic1 toprint trust http://www.citeulike.org/user/suleehs/article/2100743 <i>Cooperative Information Agents VII (2003), pp. 238-249.</i><br /><br />The so-called Web of Trust is one of the ultimate goals of the Semantic Web. Research on the topic of trust in this domain has focused largely on digital signatures, certificates, and authentication. At the same time, there is a wealth of research into trust and social networks in the physical world. In this paper, we describe an approach for integrating the two to build a web of trust in a more social respect. This paper describes the applicability of social network analysis to the semantic web, particularly discussing the multi-dimensional networks that evolve from ontological trust specifications. As a demonstration of algorithms used to infer trust relationships, we present several tools that allow users to take advantage of trust metrics that use the network. Trust Networks on the Semantic Web Jennifer Golbeck Bijan Parsia James Hendler Cooperative Information Agents VII (2003), pp. 238-249. 2007-12-12T22:11:14-00:00 2003 Cooperative Information Agents VII 238 249 recommendation topic1 toprint trust http://www.citeulike.org/user/suleehs/article/1036315 <i>: Trust Management (2004), pp. 251-265.</i> Analyzing Correlation between Trust and User Similarity in Online Communities Cai-Nicolas Ziegler Georg Lausen : Trust Management (2004), pp. 251-265. 2007-01-11T09:37:56-00:00 2004 : Trust Management 251 265 recommendation topic1 toprint trust 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/2056215 <i>(2006), pp. 1097-1101.</i> Being accurate is not enough: how accuracy metrics have hurt recommender systems Sean Mcnee John Riedl Joseph Konstan doi:10.1145/1125451.1125659 (2006), pp. 1097-1101. 2007-12-04T08:50:20-00:00 2006 1097 1101 ACM dlpaws recommendation 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/3015805 <i>Advances in Case-Based Reasoning (2002), pp. 731-737.</i><br /><br />Recommender systems combine user profiling and filtering techniques to provide more pro-active and personal information retrieval systems, and have been gaining in popularity as a way of overcoming the ubiquitous information overload problem. Many recommender systems operate as interactive systems that seek feedback from the end-user as part of the recommendation process to revise the user’s query. In this paper we examine different forms of feedback that have been used in the past and focus on a low-cost preference-based feedback model, which to date has been very much under utilised. In particular we describe and evaluate a novel comparison-based recommendation framework which is designed to utilise preference-based feedback. Specifically, we present results that highlight the benefits of a number of new query revision strategies and evidence to suggest that the popular more-like-this strategy may be flawed. Comparison-Based Recommendation Lorraine Ginty Barry Smyth doi:10.1007/3-540-46119-1_42 Advances in Case-Based Reasoning (2002), pp. 731-737. 2008-07-17T21:09:04-00:00 2002 Advances in Case-Based Reasoning 731 737 explanation recommendation toprint http://www.citeulike.org/user/suleehs/article/344219 <i>(2003), pp. 585-592.</i> Is seeing believing?: how recommender system interfaces affect users' opinions Dan Cosley Shyong Lam Istvan Albert Joseph Konstan John Riedl doi:10.1145/642611.642713 (2003), pp. 585-592. 2005-10-07T12:38:19-00:00 2003 585 592 ACM Press explanation recommendation toprint http://www.citeulike.org/user/suleehs/article/3015800 <i>(1999), pp. 268-275.</i> A personal news agent that talks, learns and explains Daniel Billsus Michael Pazzani doi:10.1145/301136.301208 (1999), pp. 268-275. 2008-07-17T21:06:02-00:00 1999 268 275 ACM explanation recommendation topic2 http://www.citeulike.org/user/suleehs/article/2282122 <i>Applied Artificial Intelligence, Vol. 17, No. 8. (2003), pp. 687-714.</i><br /><br />This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers. INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices Liliana Ardissono Anna Goy Giovanna Petrone Marino Segnan Pietro Torasso doi:10.1080/713827254 Applied Artificial Intelligence, Vol. 17, No. 8. (2003), pp. 687-714. 2008-01-23T20:56:08-00:00 2003 Applied Artificial Intelligence 17 8 687 714 Taylor & Francis explanation recommendation http://www.citeulike.org/user/suleehs/article/201419 <i>(2002), pp. 830-831.</i> The role of transparency in recommender systems Rashmi Sinha Kirsten Swearingen doi:10.1145/506443.506619 (2002), pp. 830-831. 2005-05-16T06:07:37-00:00 2002 830 831 ACM Press dlblog dlpaws explanation recommendation 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