The impacts of relevance of recommendations and goal commitment on user experience in news recommender design

Abstract

Cold start and data sparsity are problems hindering the function of news recommender systems. Optimally serving first-time users through relevant news article recommendations is an application of these problems that have attracted scholars’ attention. Users’ goal commitment might be another solution that raise efficiency of information searching while it is understudied in previous research. Drawing from the results of 669 Amazon MTurk workers’ questionnaires, this experimental study explored solutions. We manipulated the relevance of news recommendations (high relevance vs. low relevance) and information behavior within a news portal, either scanning (via a list of news articles) or seeking (via a search query). We also measured an individual difference variable, goal commitment. Results indicated that higher relevance of recommendations and higher goal commitment lead to lower information overload, higher user satisfaction, and lower information anxiety. We also found interaction effects of goal commitment and content relevance on article selection, such that users will be likely to select more irrelevant articles in the low relevance condition rather than the high relevance condition even though they have a goal commitment and perceive higher information overload and information anxiety indirectly via selecting more irrelevant articles. Furthermore, people with high goal commitment were less anxious when they read fewer irrelevant articles in the news recommender systems. The study addressed the importance of considering the user-recommender interaction and the potential merits of considering users goal commitment in the news recommender system design. The research indicates integrating personal traits into state-of-the-art news recommender systems has the potential to significantly improve user experience. While this research suggests personal traits can mitigate the limitations of imperfect recommender systems, users can also curate or train these systems based on their goals to further enhance efficiency.