A novel approach of making better recommendations by revealing hidden desires and information curation for users of internet of things

• Published in: Multimedia tools and applications Vol. 78; no. 3; pp. 3183 - 3201
• Main Authors: Lee, Keonsoo, Lee, Yang Sun, Nam, Yunyoung
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2019; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Internet of Things; Multimedia Information Systems; Recommender systems; Similarity; Special Purpose and Application-Based Systems; Information curation; Hidden desire; Browsing; Recommendation system
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6084-4

One of the most significant disadvantages of the Internet of Things (IoT) is the overload of information. More information makes it harder to find valuable information. Recommendation systems identify the most suitable items for a given user. The recommended result is only valid if the system users know what they want, and clearly and explicitly convey their needs to the system. Because the role of a recommendation system is to calculate the similarity between the given request and each item, and to rank the similarity, the requests and identity of items should be clear to obtain correct results. However, in most situations in which recommendations are made, requests are implicit and ambiguous. A good recommendation system should make a reliable list of items, even with ambiguous requests. This paper proposes a model of generating recommendations for implicit requests. The model employs two methods that reveals the desire of the requestor and uses content curation with a customized layout to display the recommendations. The first method for revealing the requestor’s desire is to specify the implicit request by combining the user’s customized preference with the collective intelligence. The second method for employing content curation is to arrange the recommendation for users to accept spontaneously. To persuade users, the recommendations are transformed into a layout based on a personalized cognitive bias. Through these processes, reliable and beneficial recommendations can be provided to any user even if their requests are implicit or unclear.