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.

中文翻译：

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基于社交数据的冷启动和长尾推荐

“点击次数”已被广泛认为是许多网络系统的命脉，例如电子商务系统、广告系统和多媒体消费系统。但是，如果用户看不到该项目，或者他们对该项目不感兴趣，则他们不会点击该项目。推荐系统在从近乎无限的库存中发现有趣的物品并将它们展示给潜在用户方面起着至关重要的作用。然而，有两个问题正在削弱推荐系统。一个是“如何处理新用户”，一个是“如何给用户带来惊喜”。前者是众所周知的冷启动推荐。在本文中，我们表明后者可以作为长尾推荐进行研究。我们还利用共同挑战冷启动和长尾推荐的好处，并提出了一种新颖的方法，可以在一个统一的目标中同时处理它们。对于冷启动问题，我们从辅助信息中学习，例如用户属性、用户社交关系等。然后，我们将学到的知识转移到新用户。对于长尾推荐，我们将整体有趣的物品分解为两部分：短头物品的低秩部分和长尾物品的稀疏部分。两部分在训练阶段独立展示，并转化为最终对新用户的推荐。此外，我们有效地将这两个问题制定为一个统一的目标，并提出了一种迭代优化算法。提出了该方法的快速扩展以降低复杂性，并提供了广泛的理论分析来证明我们方法的界限。最后，