Incorporating contextual information in recommender systems using a multidimensional approach

TLDR

The study proposes a multidimensional recommender system that incorporates contextual information beyond users and items to generate recommendations. It introduces a multidimensional rating estimation framework that selects context‑relevant two‑dimensional rating segments, applies standard collaborative filtering, compares this approach to traditional two‑dimensional methods, and combines them by choosing the superior method for each situation. A pilot empirical evaluation on a movie recommender demonstrates the combined approach’s performance.

Abstract

The article presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, profiling information, and hierarchical aggregation of recommendations. The article also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the article introduces a combined rating estimation method, which identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the article presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance.