Canonical correlation analysis (CCA) has been extensively employed in various real-world applications of multi-label annotation. However, two major challenges are raised by the classical CCA. First, CCA frequently fails to remove noisy and irrelevant features. Second, CCA cannot effectively capture correlations between multiple labels, which are especially beneficial for multi-label learning. In this paper, we propose a novel framework that integrates joint sparsity and low-rank shared subspace into the least-squares formulation of CCA. Under this framework, multiple label interactions can be uncovered by the shared structure of the input features and a few highly discriminative features can be decided via structured sparsity inducing norm. Owing to the inclusion of the non-smooth row sparsity, a new efficient iterative algorithm is derived with proved convergence. The empirical studies on several popular web image and movie data collections consistently deliver the effectiveness of our new formulation in comparison with competing algorithms.

User selection data accumulates as time goes by. Although the recent selections are usually assumed to have higher impact on the recommendation accuracy, empirical studies on this problem are limited. For old data, whether they can contribute to the recommendation accuracy is still to be determined. On one hand, changes in short-term user preference over time may limit their effectiveness in prediction, but on the other hand, one cannot rule out their potential in capturing long term user preferences. The result is important for the system owner to determine which data is useful to make the recommendation accurately. While there have been some related studies on the time dependency of data quality using neighbor-based CF methods (e.g., [4]), its effects remain unverified for other CF methods. In this paper, we study the effect of data generated over different time period on recommendation precision using several popular model-based CF algorithms (latent factor models). experiment results show that while more recent data expectedly have larger impacts, the usefulness of older data cannot be ignored as long as there are sufficient old samples. However, the addition of insufficient amount of old data seems to have negative impacts.

Recommendations generated by Content Based method are highly related to the user's previous choices, which may not only unattractive to the user[1], but also restrict the user's horizon [1], [2]. At the same time, a new paradigm of making recommendations hat "surprise" the user poses new challenges in relation to the definition, formulation and performance metrics for surprising recommendation systems. Moreover, users may demand recommendations with varying degrees of surprising ness that satisfy their personal interests on the one hand, and encourage the explorations of new or unexpected areas of potential interests on another. To meet these challenges, in this paper, we proposed a framework, called Customizable GenPref, and the associated techniques for generating customizable surprising recommendations. Specifically, we contribute to the following aspects: firstly, through a review of the related works, we distinguish the difference between surprising ness and other concepts such as diversity, unexpectedness in non-traditional recommendations, secondly, we argue that the elements of surprise in a recommendation involve two conflicting goals, namely unusuality and relevance in the recommendation and proposed a framework of making recommendations such that by tuning a user-defined parameter a a user will receive recommendations which are either similar to his/her previous choices, or different and novel that surprises him/her, or combinations of both. We have evaluated our proposed framework using several relevant performance metrics, such as accuracy and diversity. Our experimental results show that Customizable GenPref is not only able to predict and recommend similar or surprising items that the user may like, but, at the same time, also serves the business objectives of e-commerce sites by recommending more distinct items to the users compared with baseline methods.