Lorraine McGinty | University College Dublin (original) (raw)
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Papers by Lorraine McGinty
In 2009 UCD launched Irelands first taught postgraduate programme operating according to a negoti... more In 2009 UCD launched Irelands first taught postgraduate programme operating according to a negotiated learning (NL) model. While some Universities have demonstrated the effectiveness of the NL model, no European University offers a negotiated programme of learning with anything near to the breadth of flexibility covered by the taught MSc in Computer Science by Negotiated Learning.The NL programme is aimed primarily at: (1) individual students with specific workplace & continuing professional development needs, and/or (2) cohorts of students who wish to specialise in a specific area of computer science (i.e. the programme may be negotiated with a specific industry sector or on behalf of a specific group of students).Importantly, the programme is very different from the traditional “structured” postgraduate programme where students tend to have very restricted choice (if any) and are often required to take some modules that have no relevance to critical areas where they have specific learning requirements. In contrast, NL students undergo a very detailed assessment of their training-needs and individual student negotiated learning contracts are carefully customised with each student across >80 module offerings. Module offerings range from programming Java/C/C++/Ruby/Python to Data Mining, Bioinformatics and complementary modules in related disciplines(e.g. Entrepreneurship, Mathematics, Engineering, etc.). This paper describes how a programme of learning is formulated from the bottom up that carefully maps individual student skill requirements to very specific learning outcomes in view of their thematic specialisation. In addition to taking taught modules each student is required to undertake significant research practicum specific to the focus of their specialisation.This paper also highlights how the programme provider (i.e. the UCD School of Computer Science & Informatics) is developing the programme further through the establishment of learning-contacts across other Schools, Colleges and Research Centers throughout UCD. In addition, opportunties and limitations related to the cross-institution implementation of this innovative learning model are presented following a pilot sudy carried out with DCU during the academic year ‘09/’10.
International Joint Conference on Artificial Intelligence, Aug 9, 2003
Abstract. Conversational recommender systems help to guide users through a product-space towards ... more Abstract. 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 va-cation/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...
Proceedings of the 11th international conference on Intelligent user interfaces, 2006
The Knowledge Engineering Review, 2005
We describe recommender systems and especially case-based recommender systems. We define a framew... more We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.
Springer eBooks, Sep 3, 2007
Recommender systems are designed to assist users in the search for product and service informatio... more Recommender systems are designed to assist users in the search for product and service information and have been successfully deployed in a range of domains, from restaurants to route planning, movies to news. In particular, conversational recommender systems engage the user in an extended recommendation dialog, making suggestions and eliciting user feedback in order to guide the next round of recommendations. This complementary user-system interaction has led to a recent interest in mixed-initiative systems research. In this paper we examine some of the shortcomings of existing case-based conversational recommender systems. In particular, we highlight how a more flexible recommendation strategy, one that responds to intermediate recommendation success and failures, can lead to significant improvements in both the efficiency and quality of recommendation dialogs. We argue that such techniques have a role to play in mixed-initiative recommender systems in the future.
International Joint Conference on Artificial Intelligence, 2003
User feedback is vital in many recommender sys tems to help guide the search for good recommen ... more User feedback is vital in many recommender sys tems to help guide the search for good recommen dations. Preference-based feedback (e.g. "Show me more like item A ") is an inherently ambiguous form of feedback with a limited ability to guide the recommendation process, and for this reason it is usually avoided. Nevertheless we believe that cer tain domains demand
Proceedings of the 11th International Conference on Intelligent User Interfaces, Jan 29, 2006
Consumers are often overwhelmed by the range of product choices available, especially online, and... more Consumers are often overwhelmed by the range of product choices available, especially online, and recommender systems have emerged as an important tool for helping users to navigate through complex product spaces based on their preferences. In this paper we describe work that concentrates on how research ideas from two complimentary research communities (recommender systems and intelligent user interfaces) can be
In 2009 UCD launched Irelands first taught postgraduate programme operating according to a negoti... more In 2009 UCD launched Irelands first taught postgraduate programme operating according to a negotiated learning (NL) model. While some Universities have demonstrated the effectiveness of the NL model, no European University offers a negotiated programme of learning with anything near to the breadth of flexibility covered by the taught MSc in Computer Science by Negotiated Learning.The NL programme is aimed primarily at: (1) individual students with specific workplace & continuing professional development needs, and/or (2) cohorts of students who wish to specialise in a specific area of computer science (i.e. the programme may be negotiated with a specific industry sector or on behalf of a specific group of students).Importantly, the programme is very different from the traditional “structured” postgraduate programme where students tend to have very restricted choice (if any) and are often required to take some modules that have no relevance to critical areas where they have specific learning requirements. In contrast, NL students undergo a very detailed assessment of their training-needs and individual student negotiated learning contracts are carefully customised with each student across >80 module offerings. Module offerings range from programming Java/C/C++/Ruby/Python to Data Mining, Bioinformatics and complementary modules in related disciplines(e.g. Entrepreneurship, Mathematics, Engineering, etc.). This paper describes how a programme of learning is formulated from the bottom up that carefully maps individual student skill requirements to very specific learning outcomes in view of their thematic specialisation. In addition to taking taught modules each student is required to undertake significant research practicum specific to the focus of their specialisation.This paper also highlights how the programme provider (i.e. the UCD School of Computer Science & Informatics) is developing the programme further through the establishment of learning-contacts across other Schools, Colleges and Research Centers throughout UCD. In addition, opportunties and limitations related to the cross-institution implementation of this innovative learning model are presented following a pilot sudy carried out with DCU during the academic year ‘09/’10.
International Joint Conference on Artificial Intelligence, Aug 9, 2003
Abstract. Conversational recommender systems help to guide users through a product-space towards ... more Abstract. 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 va-cation/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...
Proceedings of the 11th international conference on Intelligent user interfaces, 2006
The Knowledge Engineering Review, 2005
We describe recommender systems and especially case-based recommender systems. We define a framew... more We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.
Springer eBooks, Sep 3, 2007
Recommender systems are designed to assist users in the search for product and service informatio... more Recommender systems are designed to assist users in the search for product and service information and have been successfully deployed in a range of domains, from restaurants to route planning, movies to news. In particular, conversational recommender systems engage the user in an extended recommendation dialog, making suggestions and eliciting user feedback in order to guide the next round of recommendations. This complementary user-system interaction has led to a recent interest in mixed-initiative systems research. In this paper we examine some of the shortcomings of existing case-based conversational recommender systems. In particular, we highlight how a more flexible recommendation strategy, one that responds to intermediate recommendation success and failures, can lead to significant improvements in both the efficiency and quality of recommendation dialogs. We argue that such techniques have a role to play in mixed-initiative recommender systems in the future.
International Joint Conference on Artificial Intelligence, 2003
User feedback is vital in many recommender sys tems to help guide the search for good recommen ... more User feedback is vital in many recommender sys tems to help guide the search for good recommen dations. Preference-based feedback (e.g. "Show me more like item A ") is an inherently ambiguous form of feedback with a limited ability to guide the recommendation process, and for this reason it is usually avoided. Nevertheless we believe that cer tain domains demand
Proceedings of the 11th International Conference on Intelligent User Interfaces, Jan 29, 2006
Consumers are often overwhelmed by the range of product choices available, especially online, and... more Consumers are often overwhelmed by the range of product choices available, especially online, and recommender systems have emerged as an important tool for helping users to navigate through complex product spaces based on their preferences. In this paper we describe work that concentrates on how research ideas from two complimentary research communities (recommender systems and intelligent user interfaces) can be