Recently, novel recommender systems have attracted considerable attention in the research community. Recommending popular items may not always satisfy users. For example, although most users likely prefer popular items, such items are often not very surprising or novel because users may already know about the items. Also, such recommender systems hardly satisfy a group of users who prefer relatively obscure items. Existing novel recommender systems, however, still recommend mainly popular items or degrade the quality of recommendation. They do so because they do not consider the balance between novelty and preference-based recommendation. This paper proposes an efficient novel-recommendation method called Personal Popularity Tendency Matching (PPTM) which recommends novel items by considering an individual's Personal Popularity Tendency (or PPT). Considering PPT helps to diversify recommendations by reasonably penalizing popular items while improving the recommendation accuracy. We experimentally show that the proposed method, PPTM, is better than other methods in terms of both novelty and accuracy.