118,963 research outputs found
Towards Question-based Recommender Systems
Conversational and question-based recommender systems have gained increasing
attention in recent years, with users enabled to converse with the system and
better control recommendations. Nevertheless, research in the field is still
limited, compared to traditional recommender systems. In this work, we propose
a novel Question-based recommendation method, Qrec, to assist users to find
items interactively, by answering automatically constructed and algorithmically
chosen questions. Previous conversational recommender systems ask users to
express their preferences over items or item facets. Our model, instead, asks
users to express their preferences over descriptive item features. The model is
first trained offline by a novel matrix factorization algorithm, and then
iteratively updates the user and item latent factors online by a closed-form
solution based on the user answers. Meanwhile, our model infers the underlying
user belief and preferences over items to learn an optimal question-asking
strategy by using Generalized Binary Search, so as to ask a sequence of
questions to the user. Our experimental results demonstrate that our proposed
matrix factorization model outperforms the traditional Probabilistic Matrix
Factorization model. Further, our proposed Qrec model can greatly improve the
performance of state-of-the-art baselines, and it is also effective in the case
of cold-start user and item recommendations.Comment: accepted by SIGIR 202
Finding the right answer: an information retrieval approach supporting knowledge sharing
Knowledge Management can be defined as the effective strategies to get the right piece of knowledge to the right person in the right time. Having the main purpose of providing users with information items of their interest, recommender systems seem to be quite valuable for organizational knowledge management environments. Here we
present KARe (Knowledgeable Agent for Recommendations), a multiagent recommender system that supports users sharing knowledge in a peer-to-peer environment. Central to this work is the assumption that social interaction is essential for the creation and dissemination of new knowledge. Supporting social interaction, KARe allows users to share knowledge through questions and answers. This paper describes KARe�s agent-oriented architecture and presents its recommendation algorithm
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
Dynamics of Content Quality in Collaborative Knowledge Production
We explore the dynamics of user performance in collaborative knowledge
production by studying the quality of answers to questions posted on Stack
Exchange. We propose four indicators of answer quality: answer length, the
number of code lines and hyperlinks to external web content it contains, and
whether it is accepted by the asker as the most helpful answer to the question.
Analyzing millions of answers posted over the period from 2008 to 2014, we
uncover regular short-term and long-term changes in quality. In the short-term,
quality deteriorates over the course of a single session, with each successive
answer becoming shorter, with fewer code lines and links, and less likely to be
accepted. In contrast, performance improves over the long-term, with more
experienced users producing higher quality answers. These trends are not a
consequence of data heterogeneity, but rather have a behavioral origin. Our
findings highlight the complex interplay between short-term deterioration in
performance, potentially due to mental fatigue or attention depletion, and
long-term performance improvement due to learning and skill acquisition, and
its impact on the quality of user-generated content
Student questioning : a componential analysis
This article reviews the literature on student questioning, organized through a modified version of Dillon's (1988a, 1990) componential model of questioning. Special attention is given to the properties of assumptions, questions, and answers. Each of these main elements are the result of certain actions of the questioner, which are described. Within this framework a variety of aspects of questioning are highlighted. One focus of the article is individual differences in question asking. The complex interactions between students' personal characteristics, social factors, and questioning are examined. In addition, a number of important but neglected topics for research are identified. Together, the views that are presented should deepen our understanding of student questioning
Collaborative Epistemic Discourse in Classroom Information Seeking Tasks
We discuss the relationship between information seeking, and epistemic beliefs – beliefs about the source, structure, complexity, and stability of knowledge – in the context of collaborative information seeking discourses. We further suggest that both information seeking, and epistemic cognition research agendas have suffered from a lack of attention to how information seeking as a collaborative activity is mediated by talk between partners – an area we seek to address in this paper. A small-scale observational study using sociocultural discourse analysis was conducted with eight eleven year old pupils who carried out search engine tasks in small groups. Qualitative and quantitative analysis were performed on their discussions using sociocultural discourse analytic techniques. Extracts of the dialogue are reported, informed by concordance analysis and quantitative coding of dialogue duration. We find that 1) discourse which could be characterised as ‘epistemic’ is identifiable in student talk, 2) that it is possible to identify talk which is more or less productive, and 3) that epistemic talk is associated with positive learning outcomes
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