Abstract
Recommender systems are widely used intelligent applications which assist users in a decision-making process to select one product from a huge set of alternative products or services. Recent research focus has been on developing methods for generating recommendations. We note lack of coherent research in the field of visual depictions of recommendations as well as how visualization and interactivity can aid users in use and decision-making with recommender systems. This paper proposes a novel approach to visualization of recommendations driven by preferences of the user to provide them with beneficial and persuasive recommendations. A personalized visual interface has been used considering requirements of the user and product’s utility in order to provide effective recommendations to the user. The anticipated visual interface is interactive and tailored according to user’s preferences. Users can alter their current necessities interactively in order to acquire the value-added recommendations from the Recommender System. It provides the users with a simple and most relevant set of recommendations as per their interest. This substantially reduces users’ interaction effort, especially for a sizable and complex product domain. Precision and recall metrics have been used to measure performance of the proposed system as well as users’ subjective feedback.
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Vashisth, P., Khurana, P., Bedi, P., Agarwal, S.K. (2018). Capturing User Preferences Through Interactive Visualization to Improve Recommendations. In: Deka, G., Kaiwartya, O., Vashisth, P., Rathee, P. (eds) Applications of Computing and Communication Technologies. ICACCT 2018. Communications in Computer and Information Science, vol 899. Springer, Singapore. https://doi.org/10.1007/978-981-13-2035-4_7
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