Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers’ needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment  associated to user’s intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren’t familiar with technical features, perceives usefulness is a main factor influencing users’ adoption. Meanwhile, perceived enjoyment plays a role on user’s intention to adopt this interaction model, for users who are familiar with technical features of product

Data-driven decision making in Critique-based recommenders: from a critique to social media data

In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones