Effects of anchoring process under preference stabilities for interactive movie recommendations

This study explores how the stability of users' preferences influences recommendation results and how this stability relates to the effectiveness of developing recommendation strategies. In this work, we propose an anchor-based hybrid filtering approach (AHF) to naturally measure and capture the user's preference stabilities for movie genres. That is, a pairwise preference of the genre comparison process with the genre-based fuzzy inference filtering was conducted in order to achieve effective interactive recommendations. To conduct this experiment, we recruited 30 users with different levels of preference stability for movie genres. The experimental results show that the proposed AHF approach can effectively capture the user's preferences and filter out undesired movie genres. In addition, this approach can give a more precise recommendation than one without the anchoring process, especially for the user who has unstable preferences for movie genres. Our proposed approach achieves statistical significance and outperforms the baseline method for recommending users' favorite movies by more than 63% for the stable user group and 77% for the unstable group. The results suggest that the stability of users' preferences is a factor to be considered when developing effective recommendation strategies.