Proponents as the Means to Increase the Uptake of Recommendations

Author:

Matsushima Rikako1ORCID,Hijikata Yoshinori1ORCID,Berkovsky Shlomo2ORCID

Affiliation:

1. Kwansei Gakuin University, Japan

2. Macquarie University, Australia

Funder

JSPS KAKENHI

Publisher

ACM

Reference27 articles.

1. Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors. Interacting with computers, vol. 18(3), 410-431, 2006.

2. Carrer-Neto, W., Hernández-Alcaraz, M.L., Valencia-García, R., García-Sánchez, F.: Social knowledge-based recommender system. Application to the movies domain. Expert Systems with applications, vol. 39(12), pp. 10990-1000, 2012.

3. Recommender systems: from algorithms to user experience

4. Murphy-Hill, E., Murphy, G.C.: Recommendation delivery: Getting the user interface just right. In Recommendation systems in software engineering,  pp. 223-242, Springer, 2013.

5. Jameson, A., Willemsen, M.C., Felfernig, A.: Individual and group decision making and recommender systems. In Recommender systems handbook, pp. 789-832, Springer, 2022.

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