This paper presents a novel method for interest level estimation of items via matrix completion based on adaptive user matrix construction. The proposed method introduces a new criterion for adaptively constructing a user matrix that consists of user behavior features and interest levels, which are evaluated by target users and similar users. In the estimation, the matrix completion via rank minimization using the truncated nuclear norm is applied to the constructed matrix. The proposed method enables both of the interest level estimation of the target users and the selection of the similar users suitable for the estimation by monitoring errors caused in the matrix completion algorithm. The caused errors indicate the minimum differences between the estimated interest levels and true ones, and they can be regarded as the criterion for both of the optimal estimation and the adaptive selection. Furthermore, the proposed method uses weight matrices for decreasing an influence of missing data on the estimation. Consequently, accurate estimation of the interest levels becomes feasible by using the adaptively constructed matrix. Experimental results obtained by applying the proposed method to users' behavior and interest data show the effectiveness of the proposed method.