Antecedents and Implications of User Experience from Recommender Systems in Online Environments

Creating a great user experience is one of the main goals of online stores, which actually enables them to create a lasting competitive advantage. The user experience is also one of the valuable and innovative sources of information for designing recommender systems. Given the competitive world of commerce and the high similarity of goods and services, and considering the important role of the user experience and actually creating a positive impression in the user's mind, this study aims to determine the backgrounds and consequences of user experience from recommender systems in Online environments are done. The methodology of the present study is synthetic. In the qualitative section, 20 experts were selected through semi-structured interviews through purposeful judgment sampling. Then based on qualitative data content analysis, the initial research model was presented. In the quantitative part of the study, the statistical population of the study included all users and customers of the Digikala store that used its services in March and April 2019. For this purpose, 384 samples were selected by available sampling method. LISREL software was used to analyze the data in a small section and the hypotheses were confirmed. The results indicate that there are five main categories of background factors including perceived impact experience, perceived ease of experience, perceived quality experience, perceived support experience, and perceived external experience. Perceived attitudes, perceived value, perceived trust, and perceived satisfaction were also presented as consequences of the user experience of the recommender system in online environments

The effect of preference elicitation methods on the user experience of a recommender system

To increase the user experience, preference elicitation methods used by recommender systems can be adapted to individual differences such as the level of expertise. However, we will show that the satisfaction and perceived usefulness of a recommender system also depends strongly on subtle variations of the implementation of these methods