Item recommendation using tag emotion in social cataloging services. (15th December 2017)

• Record Type: Journal Article
• Authors: Lim, Hyewon; Kim, Hyoung-Joo
• Is Part Of: Expert systems with applications. Volume 89(2017)
• Page Start: 179
• Page End: 187
• Publication Date: 2017-12-15
• Subjects: Recommendation -- Tag -- Emotion -- Social cataloging service -- High-Order Singular Value Decomposition (HOSVD); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.07.046
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose a tag-based recommendation method considering user's emotions in tags. The tag weight is based on the rating and the emotion value of the tag. The emotion value of the tag is obtained using SenticNet. We apply a High-Order Singular Value Decomposition. The evaluation shows that user's emotion plays an important role in recommendation. Abstract: Due to the overload of contents, the user suffers from difficulty in selecting items. The social cataloging services allow users to consume items and share their opinions, which influences in not only oneself but other users to choose new items. The recommendation system reduces the problem of the choice by recommending the items considering the behavior of the people and the characteristics of the items. In this study, we propose a tag-based recommendation method considering the emotions reflected in the user's tags. Since the user's estimation of the item is made after consuming the item, the feelings of the user obtained during consuming are directly reflected in ratings and tags. The rating has overall valence on the item, and the tag represents the detailed feelings. Therefore, we assume that the user's rating for an item is the basic emotion of the tag attached to the item, and the emotion of tag is adjusted by the unique emotion value of the tag. We represent the relationships between users, items, and tags as a three-order tensor and apply tensor factorization. The experimental results show that the proposed method achieves better recommendation performance than baselines.