Pub Date : 2008-04-01DOI: 10.1142/9789812797025_0012
Gulden Uchyigit, K. Clark
{"title":"An Experimental Study of Feature Selection Methods for Text Classification","authors":"Gulden Uchyigit, K. Clark","doi":"10.1142/9789812797025_0012","DOIUrl":"https://doi.org/10.1142/9789812797025_0012","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127378119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed. The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems. This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.
{"title":"Personalization Techniques and Recommender Systems","authors":"Gulden Uchyigit, Matthew Y. Ma","doi":"10.1142/6788","DOIUrl":"https://doi.org/10.1142/6788","url":null,"abstract":"The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed. \u0000 \u0000The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems. \u0000 \u0000This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0007
J. Griffith, C. O'Riordan, H. Sorensen
{"title":"Identifying and Analyzing User Model Information from Collaborative Filtering Datasets","authors":"J. Griffith, C. O'Riordan, H. Sorensen","doi":"10.1142/9789812797025_0007","DOIUrl":"https://doi.org/10.1142/9789812797025_0007","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115381917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0008
Y. Blanco-Fernández, J. Pazos-Arias, A. Gil-Solla, M. Cabrer, Martín López Nores
{"title":"Personalization Strategies and Semantic Reasoning: Working in tandem in Advanced Recommender Systems","authors":"Y. Blanco-Fernández, J. Pazos-Arias, A. Gil-Solla, M. Cabrer, Martín López Nores","doi":"10.1142/9789812797025_0008","DOIUrl":"https://doi.org/10.1142/9789812797025_0008","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"11 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129748951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0001
Barry Smyth
As online information continues to grow at an exponential rate our ability to access this information effectively does not, and users are often frustrated by how difficult it is to locate the right information quickly and easily. So-called personalization technology is a potential solution to this information overload problem: by automatically learning about the needs and preferences of users, personalized information access solutions have the potential to offer users a more proactive and intelligent form of information access that is sensitive to their long-term preferences and current needs. In this paper, we document two case-studies of the use of personalization techniques to support information browsing and search. In addition, we consider the inevitable privacy issues that go hand-in-hand with profiling and personalization techniques and highlight the importance of striking the right balance between privacy and personalization when it comes to the development and deployment of practical systems.
{"title":"Personalization-Privacy Tradeoffs in Adaptive Information Access","authors":"Barry Smyth","doi":"10.1142/9789812797025_0001","DOIUrl":"https://doi.org/10.1142/9789812797025_0001","url":null,"abstract":"As online information continues to grow at an exponential rate our ability to access this information effectively does not, and users are often frustrated by how difficult it is to locate the right information quickly and easily. So-called personalization technology is a potential solution to this information overload problem: by automatically learning about the needs and preferences of users, personalized information access solutions have the potential to offer users a more proactive and intelligent form of information access that is sensitive to their long-term preferences and current needs. In this paper, we document two case-studies of the use of personalization techniques to support information browsing and search. In addition, we consider the inevitable privacy issues that go hand-in-hand with profiling and personalization techniques and highlight the importance of striking the right balance between privacy and personalization when it comes to the development and deployment of practical systems.","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0004
K. Kabassi, M. Virvou, G. Tsihrintzis
{"title":"User Modelling Sharing for Adaptive e-Learning and Intelligent Help","authors":"K. Kabassi, M. Virvou, G. Tsihrintzis","doi":"10.1142/9789812797025_0004","DOIUrl":"https://doi.org/10.1142/9789812797025_0004","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128930058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0002
Fabio Gasparetti, A. Micarelli
{"title":"A Deep Evaluation of Two Cognitive User Models for Personalized Search","authors":"Fabio Gasparetti, A. Micarelli","doi":"10.1142/9789812797025_0002","DOIUrl":"https://doi.org/10.1142/9789812797025_0002","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132893737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0005
N. Manouselis, C. Costopoulou
{"title":"Experimental Analysis of Multiattribute Utility Collaborative Filtering on a Synthetic Data Set","authors":"N. Manouselis, C. Costopoulou","doi":"10.1142/9789812797025_0005","DOIUrl":"https://doi.org/10.1142/9789812797025_0005","url":null,"abstract":"","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133543930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1142/9789812797025_0010
A. Felfernig, E. Teppan, B. Gula
Recommender applications support decision-making processes by helping online customers to identify products more effectively. Recommendation problems have a long history as a successful application area of Artificial Intelligence (AI) and the interest in recommender applications has dramatically increased due to the demand for personalization technologies by large and successful e-Commerce environments. Knowledgebased recommender applications are especially useful for improving the accessibility of complex products such as financial services or computers. Such products demand a more profound knowledge from customers than simple products such as CDs or movies. In this paper we focus on a discussion of AI technologies needed for the development of knowledgebased recommender applications. In this context, we report experiences from commercial projects and present the results of a study which investigated key factors influencing the acceptance of knowledge-based recommender technologies by end-users.
{"title":"User Acceptance of Knowledge-based Recommenders","authors":"A. Felfernig, E. Teppan, B. Gula","doi":"10.1142/9789812797025_0010","DOIUrl":"https://doi.org/10.1142/9789812797025_0010","url":null,"abstract":"Recommender applications support decision-making processes by helping online customers to identify products more effectively. Recommendation problems have a long history as a successful application area of Artificial Intelligence (AI) and the interest in recommender applications has dramatically increased due to the demand for personalization technologies by large and successful e-Commerce environments. Knowledgebased recommender applications are especially useful for improving the accessibility of complex products such as financial services or computers. Such products demand a more profound knowledge from customers than simple products such as CDs or movies. In this paper we focus on a discussion of AI technologies needed for the development of knowledgebased recommender applications. In this context, we report experiences from commercial projects and present the results of a study which investigated key factors influencing the acceptance of knowledge-based recommender technologies by end-users.","PeriodicalId":329425,"journal":{"name":"Personalization Techniques and Recommender Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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