Research On The Recommendation Techniques And Support Systems Of Internet Micro-content

The rapid growth of internet has brought great convenience for achieving information. Meanwhile, since the internet information tends to be larger, more disordered and more decentralized, it is already beyond human's information processing capabilities. People usually feel it not easy to find the required information, namely the phenomenon of information overload. The key issue is that how to provide high quality of information with some kind of techniques in a short time. The Web2.0 technologies expand the boundary of internet information resources with user-generated-content (UGC), which makes the recommendation more difficult. So it's no longer that recommendation techniques just rely on the simple relations between users and various resource objects. What's more, the requirement for Internet information could no longer be explained only by personalized recommendation, because the influencing factors include the needs of both producers and consumers. We bring users' behavior and their cross-correlations into the recommendation system. Aiming at the crucial issues (social network influences, cold boot, expandability, human-technology interaction, etc.), our research on recommending micro-content are:(1) We propose the parameter index for micro-content filtering based on users' attention rate. Then we identify the indices influencing attention rate based on social relations analysis, and according to these, valuable information could be forecasted and filtered.(2) We establish the recommendation paths based on super-network theories. Start with the sociality of micro-content, we propose that the network for micro-content interaction and communication is a super-network, which could be divided into three levels.Through mapping from users'relations to information relations, we can achieve the hypergraph-based description of the user selection (evaluation) process and the the recommendation network by the transmission path.(3) Combining with the characteristics of micro-content nodes, we bring in accelerating genetic algorithm to optimize the recommendation paths. We regard dimensional binding targets as tag similarities of information nodes, information values based on attention rate, and the recommendation distance, obtain fitness function, consider the matter of information recommendation in its entirety and the accelerating genetic algorithm reduces decision-support complexity.(4) We bring in multi-agent techniques and establish the functional modules based on preamble analysis from the perspective of decision support systems. Multi-agent techniques expand on the capabilities of human-computer interaction and knowledge learning. So the micro-content could achieve man-machine collaboration automatically and the integration platform of micro-content recommendation is established.The background of this research is the micro-content producing and processing applications of Internet enterprises. Many theories and methods including hypergraph theories, genetic algorithm, agent techniques, decision support systems, are used in the study. The methodologies include both qualitative modeling and empirical studies, to look into the issues of techniques and support systems of micro-content recommendation.