text: This work focuses on top-k recommendation in domains where underlying data distribution shifts overtime. We propose to learn a time-dependent bias for each item over whatever existing recommendation engine. Such a bias learning process alleviates data sparsity in constructing the engine, and at the same time captures recent trend shift observed in data. We present an alternating optimization framework to resolve the bias learning problem, and develop methods to handle a variety of commonly used recommendation evaluation criteria, as well as large number of items and users in practice. The proposed algorithm is examined, both offline and online, using real world data sets collected from the largest retailer worldwide. Empirical results demonstrate that the bias learning can almost always boost recommendation performance. We encourage other practitioners to adopt it as a standard component in recommender systems where temporal dynamics is a norm.
  @cite_4: Multi-label classification is useful for text categorization, multimedia retrieval, and many other areas. A commonly used multi-label approach is the binary method, which constructs a decision function for each label. For some applications, adjusting thresholds in decision functions of the binary method significantly improves the performance, but few studies have been done on this subject. This article gives a detailed investigation on the selection of thresholds. Experiments on real-world data sets demonstrate the usefulness of some simple selection strategies.
  @cite_8: Thresholding strategies in automated text categorization are an underexplored area of research. This paper presents an examination of the effect of thresholding strategies on the performance of a classifier under various conditions. Using k-Nearest Neighbor (kNN) as the classifier and five evaluation benchmark collections as the testbets, three common thresholding methods were investigated, including rank-based thresholding (RCut), proportion-based assignments (PCut) and score-based local optimization (SCut); in addition, new variants of these methods are proposed to overcome significant problems in the existing approaches. Experimental results show that the choice of thresholding strategy can significantly influence the performance of kNN, and that the optimal'' strategy may vary by application. SCut is potentially better for fine-tuning but risks overfitting. PCut copes better with rare categories and exhibits a smoother trade-off in recall versus precision, but is not suitable for online decision making. RCut is most natural for online response but is too coarse-grained for global or local optimization. RTCut, a new method combining the strength of category ranking and scoring, outperforms both PCut and RCut significantly.
summary: Our proposed bias learning method in collaborative filtering is partly inspired from the thresholding problem in multi-class label classification @cite_8 @cite_4 . For large-scale multi-class label classification problem, one-vs-rest is still widely used. That is, for each class we construct a binary classifier by treating the class as positive, and the remaining classes as negative. Since each binary classifier is constructed independently, researchers propose to learns a threshold (bias) for each class mainly to optimize classification accuracy, precision recall or F-measure. However, in top- @math recommendation, the score difference and ranking of items matter, making all the items dependent on each other. Also, the motivation of this work is mainly to capture temporal dynamics rather than calibrating the classifier prediction scores.