On Promoting Listening Comprehension of English Majors through Original English Movies

In recent years,with the reform of College English Teaching in China,listening has occupied a great proportion of both the examination content and classroom teaching.When everyone is seeking a high-efficiency,fast teaching method,the use of English original film teaching methods arises spontaneously.Its authentic speaking style,real language environment and rich cultural connotation not only improve the listening comprehension ability of English majors,but also enable students to intuitively understand the culture of Western countries.In this study,by referring to a large number of books and documents,this paper puts forward corresponding strategies for various problems arising from the use of English original film teaching in colleges and universities.English original film teaching can not only create a better learning environment,and stimulate students’interest in learning,but also enable students to actively participate in class discussions.

A novel movies recommendation algorithm based on reinforcement learning with DDPG policy

Purpose-English original movies played an important role in English learning and communication.In order to find the required movies for us from a large number of English original movies and reviews,this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies.In fact,although the conventional movies recommendation algorithms have solved the problem of information overload,they still have their limitations in the case of cold start-up and sparse data.Design/methodology/approach-To solve the aforementioned problems of conventional movies recommendation algorithms,this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning,which uses the deep deterministic policy gradient(DDPG)algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one.Meanwhile,a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely.Findings-In order to verify the feasibility and validity of the proposed algorithm,the state of the art and the proposed algorithm are compared in indexes of RMSE,recall rate and accuracy based on the MovieLens English original movie data set for the experiments.Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators.Originality/value-Applying the proposed algorithm to recommend English original movies,DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.