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Computational model for generating interactions in conversational recommender system based on product functional requirements
Baizal Z.K.A.a, Widyantoro D.H.b, Maulidevi N.U.b
a Telkom University, Bandung, Indonesia
b Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Elsevier B.V.Conversational recommender system is a tool to help customer in deciding products they are going to buy, by conversational mechanism. By this mechanism, the system is able to imitate natural conversation between customer and professional sales support, for eliciting customer preference. However, many customers are not familiar with the technical features of multi-function and multi-feature products. A more natural way to explore customer preferences is by asking what they want to use with the product they are looking for (product functional requirements). Therefore, this paper proposes a computational model incorporating product functional requirements for interaction. The proposed model covers ontology and its structure as well as algorithms for generating interaction that comprises asking question, recommending products and presenting explanation of why a product is recommended. Based on our user studies, both expert users (familiar with product technical features) and novice users (not familiar with product technical feature) prefer our proposed interaction model than that of the flat interaction model (interaction model based on technical features). Meanwhile, functional requirements-based explanation is able to improve user trust in recommended products by 30% for novice users and 17% for expert users.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conversational recommender systems,Knowledge-based recommendations,Ontology-based,User interaction,User Modeling[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conversational recommender system,Knowledge-based recommendation,Ontology-based knowledge,Recommender systems,User interaction,User modeling[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Dwi H. Widyantoro received the B.S. degree in computer science from the Institut Teknologi Bandung (ITB), Bandung, Indonesia, in 1991. After spending two years in industry, he joined the Department of Informatics at ITB in 1993. Four years later, he was awarded a two-year fellowship from the Ministry of Education & Culture of the Republic of Indonesia to pursue an MS degree in the USA. He received the M.S. and Ph.D. degrees in computer science from Texas A&M University in 1999 and 2003, respectively. He is currently a professor and dean in the School of Electrical Engineering & Informatics at ITB. His research interests include machine learning, information summarization, information extraction, information classification as well as pattern recognition.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.datak.2020.101813[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]