Goal-Driven Collaborative Filtering - A Directional Error Based Approach

Collaborative filtering is one of the most effective techniques for making personalized content recommendation. In the literature, a common experimental setup in the modeling phase is to minimize, either explicitly or implicitly, the (expected) error between the predicted ratings and the true user ratings, while in the evaluation phase, the resulting model is again assessed by that error. In this paper, we argue that defining an error function that is fixed across rating scales is however limited, and different applications may have different recommendation goals thus error functions. For example, in some cases, we might be more concerned about the highly predicted items than the ones with low ratings (precision minded), while in other cases, we want to make sure not to miss any highly rated items (recall minded). Additionally, some applications might require to produce a top-N recommendation list, where the rank-based performance measure becomes valid. To address this issue, we propose a flexible optimization framework that can adapt to individual recommendation goals. We introduce a Directional Error Function to capture the cost (risk) of each individual predictions, and it can be learned from the specified performance measures at hand. Our preliminary experiments on a real data set demonstrate that significant performance gains have been achieved.