Make it personal: A social explanation system applied to group recommendations

• Published in: Expert systems with applications Vol. 76; pp. 36 - 48
• Main Authors: Quijano-Sanchez, Lara, Sauer, Christian, Recio-Garcia, Juan A., Diaz-Agudo, Belen
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.06.2017; Elsevier BV
• Subjects: Customization; Decision making; Expert systems; Group recommenders; Personal relationships; Personality; Personalization; Recommender systems; Social explanations; Social networks; Tie strength; Trustworthiness; User satisfaction; Social explanations; Social networks; Group recommenders; Personality; Personalization; Tie strength
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.01.045

•We propose personalized social individual explanations for group recommenders.•We propose both a textual and a graphical social explanation approach.•We study the benefits of including explanations in group recommender systems.•We study the benefits of including social components to these explanations.•Results show a significant increase in users’ intent to follow our recommendations. Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system’s output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the system’s group recommendation and explanations about the group’s social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on “tactful” explanations when addressing users’ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users’ intent (likelihood) to follow the recommendations, users’ satisfaction and the system’s efficiency and trustworthiness.