Computational model for generating interactions in conversational recommender system based on product functional requirements

Baizal, Z. K. A.; Widyantoro, Dwi H.; Maulidevi, Nur Ulfa

doi:10.1016/j.datak.2020.101813

Cited by 32 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interaction mechanism in the CRS includes dialogue, recommendations, and explanations (why a tourism site is recommended). The generation of this interaction mechanism utilizes semantic reasoning in ontology, which we have developed in previous studies [11]. In our current research, we focused on developing interaction mechanisms tailored to the tourism domain using the CRS framework in [11].…”

Section: Interaction Model Mechanismmentioning

confidence: 99%

“…In NBA, the system inquiries directly about the user's needs. In our previous study, we have developed a framework for developing CRS based on functional product requirements (high-level requirements) by integrating NBA and NBP [11]. Conversation based on functional product requirements is an appropriate approach for users who do not master the detailed technical features of the products [12].…”

Section: Introductionmentioning

confidence: 99%

“…With the question and answer mechanism, the system can be courageous as a tourism expert in an area. Our previous CRS framework [11] is a multidomain framework that can be applied in various domains by utilizing ontology as knowledge representation. Ontology plays an important role in representing the domain of the products (smartphone, laptop, car, server, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…In our previous research [11], the proposed framework was successfully adopted for CRS development in the laptop domain [17]. In this study, we apply the framework in [11] to develop CRS in the tourism sector, with readjustment in interaction mechanism and ontology domain.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tourism Destination Recommendation Using Ontology-based Conversational Recommender System

Baizal¹,

Tarwidi²,

Adiwijaya³

et al. 2021

IJCDS

Self Cite

View full text Add to dashboard Cite

Traveling is one of the human psychological needs. Many choices and lack of information about desired tourist attractions are some of the obstacles in fulfilling the need. One of the technologies to overcome these obstacles is the recommender system which can provide recommendations for the users to choose some interesting tourist attractions from several tourist destinations. A conversational recommender system (CRS) offers a way of recommending tourist destinations in a conversational mechanism. We use ontology as a representation of knowledge to generate conversational interactions, recommendations, and explanation facilities. With this ontology-based CRS, we can overcome cold start problems, and also the system can guide the users to get the desired tourist attractions. In this study, we use a combination of navigation by asking (NBA) and navigation by proposing (NBP) strategy to generate interactions on the CRS. Based on the user study, in general, users find it helpful in finding tourist destinations that suit their needs. It is because the factor of trust and perceived ease of use comes from the interaction and explanation facilities contained in the system. Besides, perceived usefulness directly affects users' interest in utilizing this CRS interaction model in the future.

show abstract

Section: Interaction Model Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tourism Destination Recommendation Using Ontology-based Conversational Recommender System

Baizal¹,

Tarwidi²,

Adiwijaya³

et al. 2021

IJCDS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Today, the internet contains a huge amount of information, which must then can be filtered to determine suitable facts for certain objectives [1]. With the expansion of online applications that using the internet as a mediator, many organizations so dramatically try to implement innovation that can read the pattern of transaction that has become difficult for an organization to analyse manually [2].…”

Section: Introductionmentioning

confidence: 99%

Interactive Recommender System for Identifying Learning Partner

Oktavia¹,

Sujarwo²

2021

IJETAE

View full text Add to dashboard Cite

— 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

show abstract

A smart system of Customer- product Interaction Life Cycle (CILC) in industrial Internet era for mass personalization from industrial practice survey: identification, definition, acquisition and parsing

Zhang

Ming

2023

J Intell Manuf

View full text Add to dashboard Cite

Computational model for generating interactions in conversational recommender system based on product functional requirements

Cited by 32 publications

References 22 publications

Tourism Destination Recommendation Using Ontology-based Conversational Recommender System

Tourism Destination Recommendation Using Ontology-based Conversational Recommender System

Interactive Recommender System for Identifying Learning Partner

A smart system of Customer- product Interaction Life Cycle (CILC) in industrial Internet era for mass personalization from industrial practice survey: identification, definition, acquisition and parsing

Contact Info

Product

Resources

About