This paper proposes a personal values modeling method that does not require attribute ratings. The proposed method is applied to memory-based and model-based collaborative filtering (CF) to demonstrate its effectiveness. A recent trend in CF is to introduce additional factors than interaction history. A rate matching rate (RMRate) has been proposed for modeling user’s personal values, and it has been shown to be effective in increasing diversity and recommending niche (long-tail or unpopular) items. However, RMRate needs an attribute-level evaluations in addition to rating (total evaluation) to items, which limits its applicability. To obtain users’ personal values model only from a rating matrix, this paper defines users’ personal values as their tendency to select popular/unpopular items and reputable/unreputable items. Ten attributes are proposed to model user’s personal values, all of which can be calculated from a rating matrix without additional information. Experimental results on four datasets show that the proposed attributes have different characteristics from the RMRate, and can improve precision, recall, and normalized discounted cumulative gain of memory-based CF and factorization machines. It is also shown that the proposed modeling method is useful for mitigating a cold-start problem.

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