Abstract
Most current network resources recommendation systems are used in a decision-making environment with low user participation, which can not effectively meet user expectations. The main reason is that the collection of user preferences is extremely difficult, which leads to the lack of information acquisition, and the lack of effective semantic similarity metric. Accordingly, this paper uses ontology for resource description to build a multiple inheritance hierarchical ontology model, and uses user preference model to generate attribute nodes, and construct a multiple inheritance graph model on account of the user personalization. Simultaneously, it construct preference transfer vector by user preference model, and then uses single-step transfer and multi-step transfer of SSTA to effectively extend evaluation from evaluated resources to the unevaluated resources. Experiment proves that SSTA has excellent recommendation effect compared with mature recommendation systems.
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