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Abstract-Recommender systems, inferring users' preferences from their historical activities and personal profiles, have been an enormous success in the last several years. Most of the existing works are based on the similarities of users, objects or both that derived from their purchases records in the online shopping platforms. Such approaches, however, are facing bottlenecks when the known information is limited. The extreme case is how to recommend products to new users, namely the so-called cold-start problem. The rise of the online social networks gives us a chance to break the glass ceiling. Birds of a feather flock together. Close friends may have similar hidden pattern of selecting products and the advices from friends are more trustworthy.In this paper, we integrate the individual's social relationships into recommender systems and propose a new method, called Social Mass Diffusion (SMD), based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. The results show that the SMD algorithm can achieve higher recommendation accuracy than the Mass Diffusion (MD) purely on the bipartite network. Especially, the improvement is striking for small degree users. Moreover, SMD provides a good solution to the cold-start problem. The recommendation accuracy for new users significantly higher than that of the conventional popularity-based algorithm. These results may shed some light on the new designs of better personalized recommender systems and information services.

In this paper, we analyze the negotiation protocol, negotiation strategy, negotiation flow and negotiation evaluation deeply, and proposes a new negotiation model of mobile E-commerce by designing new negotiation algorithms and new negotiation evaluation methods. Lastly, we analyse the system architecture of the mobile E-Commerce platform, which adopts the negotiation model, and simulate the negotiation flow through the transaction negotiation of three goods. The results show that the negotiation model, which reflects the user's intents well, can guarantee the fair of the transaction, and converges rapidly, and is of good adaptability and expansibility.