# A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network
## 1 Introduction  
E-commerce, the activity of buying or selling online, has generated significant business value, and it is becoming ever more common for both consumers and companies to purchase products or services online. The rise of various e-commerce platforms results in competition between companies, and they continue to explore new sales models. In recent years, social commerce, defined as a subset of e-commerce involving social networks, has become one of the most popular topics [1]. In social commerce, users are encouraged by a platform via financial rewards to share/diffuse/broadcast information about the various products sold on that platform via social networks. For example, Amazon and Twitter launched a seamless cross-platform shopping service, 'AmazonCart', in 2014, which allows Twitter users to purchase Amazon items in tweets while browsing Twitter. Another example is Alibaba, the largest e-commerce platform in China. A large number of Alibaba vendors promote their commodities through Sina Weibo (the largest Chinese social network). This indicates that e-commerce has evolved from a stand-alone platform to one that incorporates social information. [PERSON] et al. indicated that the rapid development of social media and web technology may have the potential to transform e-commerce from a product-oriented environment to a social and customer-centered one [2]. [PERSON] et al. found that, in social commerce, consumer-consumer interaction and consumer-seller interaction enhance consumers' intention to co-create brand value [3]. [PERSON] et al. provided evidence that social media use significantly influences the development of e-government and the diffusion of e-commerce globally [4]. Recommender systems for e-commerce companies have been well studied [5; 6]. However, most of the existing recommender systems use only information from e-commerce to make recommendations, such as consumer purchased history log or rating scores of purchased commodities. With the integration of e-commerce and social platforms, new recommender systems should be designed, and they should make full use of social network user information and product information. Some studies have begun to use social network information to improve the accuracy of the recommendations on e-commerce platforms. [PERSON] et al. proposed a web recommender system for e-commerce that traces clients and analyzes their activities on Facebook [7]. However, the user profiles are only based on keywords from the users' activity and their friends' activities. [PERSON] et al. improved the recommender system by adding social contextual information, i.e., social tags and latent information obtained by heterogeneous data mining [8]. [PERSON] et al. extracted the demographic information of both products and social network users' activities, and then leveraged the demographic information to improve the recommendation performance on e-commerce websites [9]. They also proposed a cross-platform recommender system that operates by learning both users' and products' features 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 [10]. Using these features, their work realized cross-site product recommendation and solved the cold-start problem. With the rise of AI technology, [PERSON] et al. proposed a unified framework of active transfer learning for cross-system recommendation, which used an active learning principle to construct entity correspondences across systems [11]. [PERSON] et al. integrated the fuzzy association rule and complex preference into a recommendation model to improve the efficiency of the traditional collaborative filtering recommendation algorithm [12]. However, these cross-platform recommendation methods solely rely on sparse social network data and e-commerce data. These methods do not fully integrate textual information, tagging information and behavioral information from the social network.