This study dealt with emotional responses elicited by certain products, which helped to understand the attributes of the product leading to emotional responses. Emotional Design is a way of design that is using emotions generated by people as reference and measurement. Making good use of emotional design could let the user discover resonance in the interaction between user and product, which could help the product to be more attractive to users. This research proposes to apply qualitative research method to uncover the secrets of emotional bonds between users and products This study also offered an useful tool to examine the strength and weakness of a certain product from perspective of emotion, and the insights could help designers to refine the product to become emotional attractive, thus create better user experience and bigger opportunity for the product on the market in the future.

In this paper, we propose an efficient and scalable low rank matrix completion algorithm. The key idea is to extend the orthogonal matching pursuit method from the vector case to the matrix case. We further propose an economic version of our algorithm by introducing a novel weight updating rule to reduce the time and storage complexity. Both versions are computationally inexpensive for each matrix pursuit iteration and find satisfactory results in a few iterations. Another advantage of our proposed algorithm is that it has only one tunable parameter, which is the rank. It is easy to understand and to use by the user. This becomes especially important in large-scale learning problems. In addition, we rigorously show that both versions achieve a linear convergence rate, which is significantly better than the previous known results. We also empirically compare the proposed algorithms with several state-of-the-art matrix completion algorithms on many real-world datasets, including the large-scale recommendation dataset Netflix as well as the MovieLens datasets. Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.