Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Distress risk, growth and earnings quality

We extend and complement prior work by investigating the earnings quality of firms with different financial health characteristics and growth prospects. By using three alternative measures of default likelihood and two alternative measures of growth options, without being limited to a specific event, we provide a more comprehensive setup for analysing the earnings characteristics of the universe of firms than examining distressed firms with persistent losses, dividend reductions or bankruptcy-filings. Our dataset consists of 15,049 healthy U.S. firms over the period 1990-2004. Results show that the relation between earnings quality and financial health is not monotonic. Distressed firms have a low level of earnings timeliness for bad news and a high level for good news, and manage earnings toward a positive target more frequently than healthy firms. On the other hand, healthy firms have a high level of earnings timeliness for bad news. Growth aspects play an important role in a firm's ability to manage earnings. In contrast to the findings of prior studies, growth firms have greater earnings timeliness for bad news, whereas value firms manage earnings toward a positive target more frequently than growth firms. © 2011 The Authors. Abacus© 2011 Accounting Foundation, The University of Sydney.