scholarly journals A Survey on Conversational Recommender Systems

2021 ◽  
Vol 54 (5) ◽  
pp. 1-36
Author(s):  
Dietmar Jannach ◽  
Ahtsham Manzoor ◽  
Wanling Cai ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
Language Processing ◽  
Information Overload ◽  
User Interaction ◽  
Preference Elicitation ◽  
Significant Progress ◽  
Software Applications ◽  
The Past ◽  
Elicitation Process ◽  
Ranked List

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this article, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

Recommender Systems

2010 ◽  
pp. 1221-1230
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Essential Role ◽  
Information Overload ◽  
Significant Progress ◽  
Future Directions ◽  
Goods And Services ◽  
The Right ◽  
The Web

With the explosive growth of goods and services available on the Web through e-commerce, it is increasingly difficult for consumers to find the right products or services. Recommender systems provide consumers with personalized recommendations of goods or services and thus help them find relevant goods or services in the information overload. Since they were introduced a decade ago, recommendation technologies have made significant progress. This article presents a brief overview of recommender systems as an effective and powerful personalization tool in the e-commerce environment. Current major recommendation approaches are described and reviewed within a single unifying recommendation model and future directions are also discussed. Recommender systems and technologies will continue to have an essential role in future e-commerce and e-business.

A Survey on Stream-Based Recommender Systems

2021 ◽  
Vol 54 (5) ◽  
pp. 1-36
Author(s):  
Marie Al-Ghossein ◽  
Talel Abdessalem ◽  
Anthony BARRÉ
Keyword(s):  
Recommender Systems ◽  
Adaptive Learning ◽  
Information Overload ◽  
Industrial Applications ◽  
The Past ◽  
Time Aware ◽  
Made In

Recommender Systems (RS) have proven to be effective tools to help users overcome information overload, and significant advances have been made in the field over the past two decades. Although addressing the recommendation problem required first a formulation that could be easily studied and evaluated, there currently exists a gap between research contributions and industrial applications where RS are actually deployed. In particular, most RS are meant to function in batch: they rely on a large static dataset and build a recommendation model that is only periodically updated. This functioning introduces several limitations in various settings, leading to considering more realistic settings where RS learn from continuous streams of interactions. Such RS are framed as Stream-Based Recommender Systems (SBRS). In this article, we review SBRS, underline their relation with time-aware RS and online adaptive learning, and present and categorize existing work that tackle the corresponding problem and its multiple facets. We discuss the methodologies used to evaluate SBRS and the adapted datasets that can be used, and finally we outline open challenges in the area.

User Interaction with Context-aware Recommender Systems on Smartphones

i-com ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Wolfgang Wörndl ◽  
Béatrice Lamche
Keyword(s):  
Recommender Systems ◽  
Mobile Devices ◽  
User Experience ◽  
Evaluation Criteria ◽  
User Interaction ◽  
Objective Evaluation ◽  
Preference Elicitation ◽  
Context Aware ◽  
Mobile Context ◽  
Selection Of

SummaryIn this article we give an overview on selected aspects of user interaction with context-aware recommender systems on smartphones. We discuss these according to the three steps of user interaction with recommender systems using subjective and objective evaluation criteria: 1. Preference elicitation: how input methods on mobile devices can influence the users’ rating behavior, 2. Result delivery and presentation: how results can be adapted to the mobile context, 3. Feedback, critiquing and refinement: how interactive explanation can improve the user experience. The selection of examples is based on several studies we did in different mobile scenarios.

Machine learning for rediscovering revolutionary ideas of the past

2021 ◽  
pp. 105971232098304
Author(s):  
R Alexander Bentley ◽  
Joshua Borycz ◽  
Simon Carrignon ◽  
Damian J Ruck ◽  
Michael J O’Brien
Keyword(s):  
Language Processing ◽  
Scientific Progress ◽  
Phylogenetic Inference ◽  
The Past ◽  
New Methods ◽  
Inherent Nature ◽  
Social Learning Processes ◽  
High Level ◽  
Scientific Ideas ◽  
Revolutionary Science

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

DESIGN DISCOURSE AND THE COGNITIVE SCIENCE OF DESIGN

2014 ◽  
Vol 79 (1) ◽  
pp. 37-53
Author(s):  
Jeroen de Ridder
Keyword(s):  
Cognitive Science ◽  
Science And Religion ◽  
The Other ◽  
Significant Progress ◽  
The Past ◽  
The One ◽  
Bare Bones ◽  
Single Focus

Much of Alvin Plantinga’s Where the Conflict Really Lies(2011) will contain few surprises for those who have been following his work over the past decades. This —I hasten to add — is nothing against the book. The fact alone that his ideas on various topics, which have appeared scattered throughout the literature, are now actualized, applied to the debate about the (alleged) conflict between science and religion, and organized into an overarching argument with a single focus makes this book worthwhile. Moreover, I see this book making significant progress on two opposite ends of the spectrum of views about science and religion. On the one end, we find the so-called new atheists and other conflict-mongers. Compared to the overheated rhetoric that oozes from their writings, this book is a breath of fresh air. Plantinga cuts right to the chase and soberly exposes the bare bones of the new atheists’ arguments. It immediately becomes clear how embarrassingly bare these bones really are. On the other end of the spectrum are theologians and scientists who envisage harmony and concord between science and religion.

scholarly journals Citizen Participation and Machine Learning for a Better Democracy

2021 ◽  
Author(s):  
Robert Procter ◽  
Miguel Arana-Catania ◽  
Felix-Anselm van Lier ◽  
Nataliya Tkachenko ◽  
Yulan He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Citizen Participation ◽  
Language Processing ◽  
Information Overload ◽  
City Council ◽  
Decision Making Processes ◽  
Development Goals ◽  
Democratic Systems ◽  
The City

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens? experience of digital citizen participation platforms. Taking as a case study the ?Decide Madrid? Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

scholarly journals Calcium Signaling in Plant Programmed Cell Death

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1089
Author(s):  
Huimin Ren ◽  
Xiaohong Zhao ◽  
Wenjie Li ◽  
Jamshaid Hussain ◽  
Guoning Qi ◽  
...  
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Abiotic Stresses ◽  
Animal Studies ◽  
Future Research ◽  
Significant Progress ◽  
Biotic And Abiotic Stresses ◽  
Stress Perception ◽  
The Past

Programmed cell death (PCD) is a process intended for the maintenance of cellular homeostasis by eliminating old, damaged, or unwanted cells. In plants, PCD takes place during developmental processes and in response to biotic and abiotic stresses. In contrast to the field of animal studies, PCD is not well understood in plants. Calcium (Ca2+) is a universal cell signaling entity and regulates numerous physiological activities across all the kingdoms of life. The cytosolic increase in Ca2+ is a prerequisite for the induction of PCD in plants. Although over the past years, we have witnessed significant progress in understanding the role of Ca2+ in the regulation of PCD, it is still unclear how the upstream stress perception leads to the Ca2+ elevation and how the signal is further propagated to result in the onset of PCD. In this review article, we discuss recent advancements in the field, and compare the role of Ca2+ signaling in PCD in biotic and abiotic stresses. Moreover, we discuss the upstream and downstream components of Ca2+ signaling and its crosstalk with other signaling pathways in PCD. The review is expected to provide new insights into the role of Ca2+ signaling in PCD and to identify gaps for future research efforts.

scholarly journals RECENT ADVANCES IN SODIUM INTERCALATION POSITIVE ELECTRODE MATERIALS FOR SODIUM ION BATTERIES

2013 ◽  
Vol 06 (01) ◽  
pp. 1330001 ◽  
Author(s):  
JING XU ◽  
DAE HOE LEE ◽  
YING SHIRLEY MENG
Keyword(s):  
Electrode Materials ◽  
High Energy ◽  
Positive Electrode ◽  
Sodium Ion ◽  
Sodium Ion Batteries ◽  
Significant Progress ◽  
Layered Oxides ◽  
Solid State Physics ◽  
The Past ◽  
Positive Electrode Materials

Significant progress has been achieved in the research on sodium intercalation compounds as positive electrode materials for Na-ion batteries. This paper presents an overview of the breakthroughs in the past decade for developing high energy and high power cathode materials. Two major classes, layered oxides and polyanion compounds, are covered. Their electrochemical performance and the related crystal structure, solid state physics and chemistry are summarized and compared.

Text Simplification

2018 ◽  
Author(s):  
Horacio Saggion
Keyword(s):  
Language Processing ◽  
Language Resources ◽  
The Past ◽  
Text Simplification ◽  
Text Readability ◽  
Target User ◽  
Evaluation Approaches ◽  
Linguistic Impairment ◽  
Automatic Text ◽  
The Web

Over the past decades, information has been made available to a broad audience thanks to the availability of texts on the Web. However, understanding the wealth of information contained in texts can pose difficulties for a number of people including those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Text simplification was initially conceived as a technology to simplify sentences so that they would be easier to process by natural-language processing components such as parsers. However, nowadays automatic text simplification is conceived as a technology to transform a text into an equivalent which is easier to read and to understand by a target user. Text simplification concerns both the modification of the vocabulary of the text (lexical simplification) and the modification of the structure of the sentences (syntactic simplification). In this chapter, after briefly introducing the topic of text readability, we give an overview of past and recent methods to address these two problems. We also describe simplification applications and full systems also outline language resources and evaluation approaches.

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