Conversational Recommender Systems and natural language:

Iovine, Andrea; Narducci, Fedelucio; Semeraro, Giovanni

doi:10.1016/j.dss.2020.113250

Cited by 68 publications

(42 citation statements)

References 11 publications

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“…The recommendation process performed by a recommender system that follows some steps: learning topic specification, preference acquisition based on higher education expectation of future job, profile building from learning partner in the LinkedIn social media, exploring learning partner competency, generation of a list of recommendation, and user evaluation and confirmation of the recommendation. It is important for organizations to get to know that the components and the recommendation steps are not the same things, the former stated that different moments in the user recommender interaction [28].…”

Section: Methodsmentioning

confidence: 99%

Interactive Recommender System for Identifying Learning Partner

Oktavia¹,

Sujarwo²

2021

IJETAE

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— Currently, higher education must open their mindset to change the learning process. This process cannot still stay on a conventional process that only involved students and lecturers as learning participants because to capture what the current industry needs and the trend of knowledge, higher education institutions must collaborate with external parties as learning partners to give a global perspective about the industrial needs and trends. The process to identify a learning partner to contribute to the learning process is not easy way. The higher education institution must select which partner has appropriate skills and competency suitable for the subject’s course. Many parameters will be involved in the selection process to identify the right partner. In this proposed system, external parties in this context consist of professionals or educators from the external institution easily can be defined to be selected as learning partners based on their competency and experience, that listed on social media LinkedIn as a professional platform. The method to build this interactive recommender system in this study is based on design science research that identifies step by step design stage to propose the best way solution for the recommender system. The result of this study is an interactive recommender system that can help higher education to find out the best candidate for their learning partner so the collaboration of learning can be implemented effectively

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Section: Methodsmentioning

confidence: 99%

Interactive Recommender System for Identifying Learning Partner

Oktavia¹,

Sujarwo²

2021

IJETAE

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“…Previous works mostly focus on static data of reviews, text content or item descriptions. As the digital voice systems such as Siri, Google home are becoming more and more mature [192], an interactive recommender system with voice feedback is a new direction where natural language processing techniques will play an important role.…”

Section: Natural Language Processing In Recommender Systemsmentioning

confidence: 99%

Artificial intelligence in recommender systems

Zhang

Jin

2020

Complex Intell. Syst.

213

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Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

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“…These platforms often allow customers to import documents to create a knowledge base, modify the knowledge base and to customize the inferred conversation design. Recent works also propose frameworks to address specific domains or tasks, such as recommendation 19 .…”

Section: Conversational Information Retrievermentioning

confidence: 99%

Chatbot Integration in Few Patterns

Báez

Daniel

Casati

et al. 2021

IEEE Internet Comput.

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Chatbots are software agents that are able to interact with humans in natural language. Their intuitive interaction paradigm is expected to significantly reshape the software landscape of tomorrow, while already today chatbots are invading a multitude of scenarios and contexts. This article takes a developer's perspective, identifies a set of architectural patterns that capture different chatbot integration scenarios, and reviews state-ofthe-art development aids. This paper was written with the precious contribution of co-author Florian Daniel, who passed away a few days after completing this manuscript for submission. We remember him here. 1

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Conversational Recommender Systems and natural language:

Cited by 68 publications

References 11 publications

Interactive Recommender System for Identifying Learning Partner

Interactive Recommender System for Identifying Learning Partner

Artificial intelligence in recommender systems

Chatbot Integration in Few Patterns

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