Abstract

Consumer and more generally, human preferences are highly complex, depending on a multitude of factors, most of which are not crisp, but uncertain/fuzzy in nature. Thus, user selection amongst a set of items is dependent on the complex comparison of items based on a large number of imprecise item-attributes such as price, size, colour, etc. This paper proposes the mechanisms to underpin the digital replication of such complex preference-based item selection with the view to enabling improved digital item search and recommendation systems. For example, a user may query “I would like a product of similar size but at a cheaper price.” The proposed method involves splitting query-attributes into two categories; those to remain similar (e.g., size) and those to be changed in a specific direction (e.g., price - to be lower). A combination of similarity and distance measures is then used to compare and rank recommendations. Initial results are presented indicating that the proposed method is effective at ranking items according to intuition and expected user preferences.