Recommender Systems are receiving substantial attention in several application areas (such as healthcare systems and e-commerce), where each area has different requirements. These systems are multifaceted by nature. So, many metrics, which are sometimes contradictious, are introduced to assess different aspects. The existence of several alternatives and dimensions to recommendation approaches complicate the evaluation of recommender systems. In such a situation, it is desirable to evaluate and compare recommenders in a united way that assesses the multifaceted aspects of these systems fairly and uniformly. Despite the abundance of evaluation dimensions, the literature still lacks an evaluation method that evaluates the multiple properties of these systems, all at once. As a potential solution, this paper proposes an evaluation methodology that provides a multidimensional assessment of recommender systems. The proposed method, which we call ComPer, combines the most common evaluation dimensions into a single, yet, general evaluation metric. ComPer is inspired by the idea that a recommender system mimics human beings; hence, it can be seen as a human and its outputs can be assessed as human’s outputs. Up to our knowledge, this is the first evaluation approach that deals with recommenders as humans. ComPer aims to be thorough (by combining multiple dimensions), simple (by presenting the final result as a single value), and independent (by providing setting-independent results). The applicability of the proposed methodology is evaluated empirically using three different datasets. The initial results are promising in the sense that ComPer is able to give comparable results regardless of the experimental settings.

A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts have been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems.
Several methods have been used to incorporate contextual information into traditional recommendation algorithms and data modeling techniques. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts.
We develop two methods: the first method attaches user preference across multiple contextual conditions, assuming that user preference remains the same, but the suitability of items differs across different contextual conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes.
We perform some experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.