Three-way Naive Bayesian collaborative filtering recommendation model for smart city

Smart city construction has penetrated all aspects of human life. People can continue to feel the convenience of life brought by new technologies and new services. In order to further optimize the smart city system, aiming at the sparsity of scoring data and the difficulty of two-way decisions to deal with uncertain decisions in the recommendation algorithm, a three-way Naive Bayesian Collaborative Filtering Recommendation model (3NBCFR) for smart city is constructed by integrating Naive Bayesian, three-way decisions and collaborative filtering algorithm. Firstly, we consider the influence of item attributes on user scoring. Naive Bayesian classifier is used to score unrated items, which can effectively fill in the missing values in the score matrix and solve the problem of data sparsity. Secondly, to ensure sustainable recommendations for uncertain goals, the three-way decisions is introduced into the collaborative filtering recommendation system, three-way recommendation rules are formulated, and 3NBCFR is constructed. Finally, the model is applied to the movie recommendation system. The experiment on Movielens shows that compared with the traditional collaborative filtering recommendation algorithm, 3NBCFR algorithm reduces the recommendation cost and improves the recommendation quality. At the same time, it lays a foundation for promoting the construction of smart city.