Research On Music Recommendation Method Combined With Time Effect

With the fast development of the Internet and digital music,various music platforms provide users with a large number of music works.However,with the quick increase in the number of music works,users face a large amount of song information,and it is difficult for users to fast find the music they are interested in.In order to provide users with a good experience and increase user satisfaction with music platforms,various music platforms use recommendation systems to provide users with personalized recommendation services.Because the user's interest is constantly changing,and over time,there will be a forgetting phenomenon,which in turn affects the user's current interest.However,when common recommendation systems make personalized recom-mendations,they rarely consider the time factor.In order to enable users to find music works they are interested in in the huge music data,this article incorporates the impact of forgetting on interests into personalized music recommendations.Algorithm to improve the quality of recommendations.The paper takes into consideration the impact of time components,and proposes two music recommendation models based on time decay functions.One is a collaborative filtering music recommendation model based on time decay functions: first,based on the Ebbinghaus forgetting curve,fitting an exponential type time decay function and power function time decay function.Secondly,a reasonable scoring mechanism is established according to the distribution of the frequency of the songs listened to by users,and then the scoring similarity and song similarity are calculated according to the modified cosine similarity formula,and the two are merged to obtain the song's Comprehensive similarity,and then introduce the time decay function to obtain the comprehensive similarity of music,and then make the score prediction;the second is the Light GBM music recommendation model based on the time decay function: firstly,the user's score is attenuated and corrected by the time decay function,and then the Light GBM model is used to predict the score,Recommend when the score is greater than or equal to the threshold.On the public music data set Last.fm,the two proposed personalized music recommendation algorithms based on time decay function are evaluated through experiments.Experiments show that in model 1,the collaborative filtering recommendation algorithm(TDF-CF)with the introduction of a time decay function is better than the traditional collaborative filtering algorithm(CF),and the collaborative filtering recommendation algorithm with the introduction of a power function-type time decay function is better.In the second model,the recommendation effect of Light GBM music recommendation algorithm(TDF-LGBM)with time decay function is better than that of music recommendation algorithm(CF)without time effect,and Light GBM music recommendation algorithm with power function time decay function is introduced Better results.Finally,the experimental results of the two models are compared and analyzed.The recommendation effect of the TDF-LGBM music recommendation algorithm incorporating the time effect is better than that of the TDF-CF music recommendation algorithm,and the power function type forgetting curve is more advantageous for recommendation.On the public music data set Last.fm,the two proposed personalized music recommendation algorithms based on time decay function are evaluated through experiments.Experiments show that the collaborative filtering recommendation algorithm(TDF-CF)that introduces the time decay function is better than the traditional collaborative filtering algorithm(CF),and the collaborative filtering recommendation algorithm that introduces the power function time decay function is better;the time decay function is introduced The recommendation effect of the Light GBM music recommendation algorithm(TDF-LGBM)is better than that of the traditional collaborative filtering algorithm(CF)that does not introduce time effects,and the Light GBM music recommendation algorithm that introduces a power function time decay function is better.The comparative analysis of the experimental results of the last two models shows that the recommendation effect of the TDF-LGBM music recommendation algorithm incorporating the time effect is better than that of the TDF-CF music recommendation algorithm,and the power function type forgetting curve is more advantageous for recommendation.The recommendation result of the TDF-LGBM algorithm that introduces the power function decay function is the best.Therefore,combining time effect and Light GBM algorithm modeling music recommendation can improve the accuracy of music recommendation and provide target users with music works that are more in line with their preferences.