A Three-way Classification with Game-theoretic N-Soft Sets for Handling Missing Ratings in Context-aware Recommender Systems

Context-aware recommender system (CARS) plays a vital role to paved way for improving traditional recommendation phenomena. A key issue in CARS is the selection of influencing contextual group which is most suitable for the recommendation of an item. In general, the selection of suitable contextual group in CARS is faced with challenges due to uncertainty in the classification of items with missing non-binary ratings. The underlying reaction of each user towards an item is implicitly assumed to be binary in nature because of which uncertainty occurs in the item classification. In particular, such a situation where ratings are missing for an item, make the exploitation of appropriate contextual group difficult for an item recommendation. In this article, we address the problem of the inappropriate classification of items into irrelevant contextual groups due to missing non-binary ratings. To this extent, we propose a three-way classification model using game-theoretic N-soft sets for improving the classification process by handling missing ratings. In particular, a game is formulated using game-theoretic N-soft sets to determine the effective threshold configuration used to induce three-way classification of items with missing non-binary ratings. Moreover, a thorough evaluation of our proposed model is carried out on the datasets of LDOS-CoMoDa and InCarMusic, where outcome signifies the effectiveness and performance of the proposed model.