Modelling personal preferences for Top-N movie recommendations

Abstract

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

CCSMR: A Combinatorial Category Space-Based Model for Recommendation

“…WENG et al proposed a new recommendation model, which studied the implicit relationships between user's item preference and additional category preference together to alleviate the cold start problem [18]. Ren et al defined the user preference as a time-ordered distribution sequence on item categories to complete T0P-N recommendation [19], [20]. A drama was represented by the sequences of subcategories and was recommended using similarities between the user interest and drama [32].…”