Information recommendation systems are used to provide suitable information to users. Conventional systems entail the difficulty that once an item is selected, only similar items are recommended. This tendency leads to user “boredom” with the recommendations and markedly reduces the recommendation effectiveness. Therefore, the present study focuses on “serendipity”, an indicator such as unexpectedness in information recommendation, to eliminate user boredom. Novelty and diversity are used to evaluate serendipity, and the unique preferences of the target user, which have not been considered in previous research, are taken into account. The conventional method, existing method from previous studies and six proposed methods are then compared. The findings indicate that the best results were obtained by recommending items that other users did not like but which had a high predicted evaluation value. This method resulted in all recommended items having novelty, and the diversity was also improved by about 14% compared to the conventional method, and serendipity was improved the most. Furthermore, it maintained the same recommendation accuracy as the conventional method. This method is expected to improve user satisfaction.