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An experiment in building profiles in information filtering: the role of context of user relevance feedback

Luz M Quiroga, Javed Mostafa
2002 Information Processing & Management  
An experiment was conducted to see how relevance feedback could be used to build and adjust profiles to improve the performance of filtering systems.  ...  The low performance obtained by the implicit acquisition mode motivated the second phase of the study, which aimed to clarify the role of context in relevance feedback judgments.  ...  The role of context in the process of retrieval, filtering and organization of information Since the mid-1960s researchers in IR have experimented with the use of relevance feedback to reformulate a query  ... 
doi:10.1016/s0306-4573(01)00058-9 fatcat:ohh7fifxfbasrpy4y352cuo43i

Agent mediated collaborative web page filtering [chapter]

Shaw Green, Pádraig Cunningham, Fergal Somers
1998 Lecture Notes in Computer Science  
A prototype system was built using this architecture, which draws upon a user's innate ability to determine the relevance of web pages to their own information needs.  ...  Intelligent filtering of multimedia documents such as World Wide Web (WWW) pages is an extremely difficult task to automate.  ...  In the case of Amalthaea the information available is in the form of user feedback on presented links.  ... 
doi:10.1007/bfb0053685 fatcat:3iv4mo27uneptloo2hnafqbcb4

Interactive collaborative filtering

Xiaoxue Zhao, Weinan Zhang, Jun Wang
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback.  ...  This requires us not to distinguish between the stages of collecting information to construct the user profile and making recommendations, but to seamlessly integrate these stages together during the interactive  ...  The scenario is also different from common relevance feedback in information retrieval, which normally handles the feedback in one or two iterations and lacks an established way to balance the exploration  ... 
doi:10.1145/2505515.2505690 dblp:conf/cikm/ZhaoZW13 fatcat:tzufqxtwtzffxjcimiqnqnbnfy

Burst the Filter Bubble: Using Semantic Web to Enable Serendipity [chapter]

Valentina Maccatrozzo
2012 Lecture Notes in Computer Science  
Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests.  ...  However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like.  ...  Acknowledgments This research is supported by the FP7 STREP "ViSTA-TV" project, as well as partially supported by the FP7 IP "NoTube" project and the ONR Global NICOP "COMBINE" project.  ... 
doi:10.1007/978-3-642-35173-0_28 fatcat:5ia7k36ph5havpjo4bfeaoepra

AUTONOMOUS AGENTS FOR WEB PAGES FILTERING

FLÁVIA COIMBRA DELICATO, LUCI PIRMEZ, LUIZ FERNANDO RUST DA COSTA CARMO
2001 Computing and Information Technologies  
With the current growth of the information available in Internet, users are facing an information overload. This work proposes a multiagent system for Web pages personalized filtering.  ...  The system is composed of a set of autonomous and adaptive agents that automatically provide relevant documents to the user according to a preferences profile.  ...  Information filtering systems assist users by filtering the data stream and delivering the relevant information to the user.  ... 
doi:10.1142/9789812810885_0002 fatcat:mykdhh62s5f7dekjk7xalwymvq

DATING THROUGH THE FILTERS

Karim Nader
2020 Social Philosophy and Policy  
Collaborative filtering algorithms can predict the preferences of a target user by looking at the past behavior of similar users.  ...  As collaborative filtering algorithms learn from these patterns to predict preferences and build recommendations, they can homogenize the behavior of dating app users and exacerbate biased sexual and romantic  ...  In other words, by collecting data on the preferences of users collectively, the algorithm predicts the preferences of an individual user, builds recommendations that match those preferences, and filters  ... 
doi:10.1017/s0265052521000133 fatcat:5ikbhzlc6nh4rdeplbuarthy7q

Text Categorization for Internet Content Filtering

José M. Gómez, Ignacio Giráldez, Manuel de Buenaga
2004 Inteligencia Artificial  
Text Filtering is one of the most challenging and useful tasks in the Multilingual Information Access field.  ...  In a number of filtering applications, Automated Text Categorization of documents plays a key role.  ...  Filtering by Text Categorization Text Filtering (TF) is a text classification task in which a "system sifts through a stream of arriving information to find documents relevant to a set of user profiles  ... 
doi:10.4114/ia.v8i22.812 fatcat:wxhmcs2pmjh3bf3eshkserfqde

Context-Aware Collaborative Filtering System: Predicting the User's Preference in the Ubiquitous Computing Environment [chapter]

Annie Chen
2005 Lecture Notes in Computer Science  
We extend collaborative filtering techniques so that what other like-minded users have done in similar context can be used to predict a user's preference towards an activity in the current context.  ...  In this paper we present a context-aware collaborative filtering system that predicts a user's preference in different context situations based on past experiences.  ...  Building a User Profile The first stage of a CF process is to build user profiles from feedback (generally in the form of ratings) on items made over time.  ... 
doi:10.1007/11426646_23 fatcat:nimvizb7gjcd3mmobyqexa2fom

Detection of shifts in user interests for personalized information filtering

W. Lam, S. Mukhopadhyay, J. Mostafa, M. Palakal
1996 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '96  
While the lower level consists of a standard convergence-type machine learning algorithm, the higher level uses Bayesiart analysis of the user provided relevance feedback to detect shifts in user interests  ...  Several machine learning approaches have been proposed in the literature to automatically learn user interests for information filtering.  ...  The objective is to estimate the user profile from the feedback data so that the filtering system can effectively choose and present information as relevant to the user as possible.  ... 
doi:10.1145/243199.243279 dblp:conf/sigir/LamMMP96 fatcat:agxegvm5tveltc62l2acgrtpne

Active Dual Collaborative Filtering with Both Item and Attribute Feedback

Luheng He, Nathan Liu, Qiang Yang
2011 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Active collaborative filtering (active CF) tries to solve this problem by intelligently soliciting user feedback in order to build an initial user profile with minimal costs.  ...  measured in terms of number of queries.  ...  Acknowledgements The authors would like to thank the support from NEC China Lab, Hong Kong RGC project 621010, and Hong Kong RGC/NSFC joint project N HKUST 624/09.  ... 
doi:10.1609/aaai.v25i1.8085 fatcat:qphaxi33xnggbjccnjbqgmdg6e

Agentized, Contextualized Filters for Information Management [chapter]

David A. Evans, Gregory Grefenstette, Yan Qu, James G. Shanahan, Victor M. Sheftel
2004 Lecture Notes in Computer Science  
We present a conceptual model of Agentized, Contextualized Filters (ACFs)-agents that identify an appropriate context for an information object and then actively fetch and filter relevant information concerning  ...  the information object in other information sources the user has access to.  ...  The authors remain solely responsible for any deficiencies in the work and in the interpretation of the good advice of others.  ... 
doi:10.1007/978-3-540-24612-1_16 fatcat:v6wktgawczfr3e2kz3mwnvgi5e

Filtering for Profile-Biased Multi-document Summarization [chapter]

Sana Leila Châar, Olivier Ferret, Christian Fluhr
2005 Lecture Notes in Computer Science  
In this article, we present an information filtering method that selects from a set of documents their most significant excerpts in relation to a user profile.  ...  The topical analysis is also used for expanding a profile in relation to a particular document by selecting the terms of the document that are closely linked to those of the profile.  ...  The detection of inferred terms is quite similar to the blind relevance feedback used in Information Retrieval.  ... 
doi:10.1007/978-3-540-31865-1_10 fatcat:hhi7lj4e55c2jct5zko6syksl4

A multilevel approach to intelligent information filtering: model, system, and evaluation

J. Mostafa, S. Mukhopadhyay, M. Palakal, W. Lam
1997 ACM Transactions on Information Systems  
The user's interests are automatically learned with only limited user intervention in the form of optional relevance feedback for documents.  ...  In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream must be handled efficiently.  ...  ACKNOWLEDGMENTS The authors would like to express their sincere appreciation of the assistance provided by Derek Kern, Tanya Blekher, and Todd Kirk in developing the prototype of SIFTER.  ... 
doi:10.1145/263479.263481 fatcat:cvofbifnjbhsljgdvt5yktb4vi

COLLABORATIVE FILTERING: A NEW APPROACH TO SEARCHING DIGITAL LIBRARIES

Janet Webster, Seikyung Jung, Jon Herlocker
2004 New Review of Information Networking  
in the OSU Libraries.  ...  , and information users to develop more useful search interfaces and increase access to the libraries' resources and services.  ...  Research Experiences for Undergraduates (REU) programme.  ... 
doi:10.1080/13614570500054229 fatcat:3kszrlf6mjfdvp2nl5xbpedp4u

A network-based model for high-dimensional information filtering

Nikolaos Nanas, Manolis Vavalis, Anne De Roeck
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
We argue that the complexity and dynamics of Information Filtering require user profile representations which are resilient and resistant to this "curse of dimensionality".  ...  The Vector Space Model has been and to a great extent still is the de facto choice for profile representation in contentbased Information Filtering.  ...  IF has an important role to play in achieving personalised information delivery to ensure that the right information reaches the right people.  ... 
doi:10.1145/1835449.1835485 dblp:conf/sigir/NanasVR10 fatcat:y27kbjl2d5g2beg5xfhqivprve
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