On Both Cold-Start and Long-Tail Recommendation with Social Data

The number of "hits" has been widely regarded as the lifeblood of many web systems, e.g., e-commerce systems, advertising systems and multimedia consumption systems. However, users would not hit an item if they cannot see it, or they are not interested in the item. Recommender system plays a critical role of discovering interesting items from near-infinite inventory and exhibiting them to potential users. Yet, two issues are crippling the recommender systems. One is "how to handle new users", and the other is "how to surprise users". The former is well-known as cold-start recommendation. In this paper, we show that the latter can be investigated as long-tail recommendation. We also exploit the benefits of jointly challenging both cold-start and long-tail recommendation, and propose a novel approach which can simultaneously handle both of them in a unified objective. For the cold-start problem, we learn from side information, e.g., user attributes, user social relationships, etc. Then, we transfer the learned knowledge to new users. For the long-tail recommendation, we decompose the overall interesting items into two parts: a low-rank part for short-head items and a sparse part for long-tail items. The two parts are independently revealed in the training stage, and transfered into the final recommendation for new users. Furthermore, we effectively formulate the two problems into a unified objective and present an iterative optimization algorithm. A fast extension of the method is proposed to reduce the complexity, and extensive theoretical analysis are provided to proof the bounds of our approach. At last, experiments of social recommendation on various real-world datasets, e.g., images, blogs, videos and musics, verify the superiority of our approach compared with the state-of-the-art work.