A generic hybrid recommender system based on neural networks
Advance Computing Conference（2014）
摘要
Content based recommender systems have the drawback of recommending only similar items to a user's particular taste, irrespective of the item's popularity. Collaborative Filtering based systems face the problem of data sparsity and expensive parameter training. In this paper, a combination of content-based, model and memory-based collaborative filtering techniques is used in order to remove these drawbacks and to present predicted ratings more accurately. The training of the data is done using feedforward backpropagation neural network and the system performance is analyzed under various circumstances like number of users, their ratings and system model.
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关键词
backpropagation,collaborative filtering,content-based retrieval,feedforward neural nets,recommender systems,collaborative filtering based systems,content based recommender systems,content-based collaborative filtering technique,data sparsity,feedforward backpropagation neural network,generic hybrid recommender system,memory-based collaborative filtering technique,model-based collaborative filtering technique,parameter training,system performance analysis,machine learning,neural networks
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