The Design And Implementation Of New Recommendation System Based On Content And Temporal Behaviour

With the rapid development of the Internet,the number of news articles has exploded,causing serious problems of information overload for users,Personalized news recommendation has helped alleviate this issue by mining user interests based on their historical behavior and recommending news relevant to them.However,current news recommendation systems still face challenges such as cold-start problem,user interest change,timeliness,user real-time feedback,and diversity.To address these challenges,this paper studies news recommendation systems based on content features and temporal behavior,including:(1)We propose a personalized news recommendation model based on content and recency.Multiple features such as news titles,abstracts,categories,and news age are captured using attention mechanisms to alleviate the coldstart problem.Multi-head self-attention and position encoding are used to represent user preferences based on clicked and impressed non-clicked news articles,which have better parallelism and handle longer sequences more effectively than RNNs,capturing user interest changes and real-time user feedback.By utilizing news age,we align the news release time with the user-click time,effectively addressing the timeliness issue of news recommendation.(2)To alleviate the user cold-start problem,we propose a news popularity prediction algorithm using news click-through rate,content,and news age to predict news popularity,We consider the changes in news popularity over its lifecycle and use attention mechanisms to learn the weight of each news article based on its popularity,adapting to personalized recommendation and balancing personalized content and popular content according to user browsing records and different users’needs.(3)We propose a context-aware diverse news recommendation model.We model the diversity task as a ranking score prediction task.For the candidate news list generated by the previous news recommendation model,we use multihead attention mechanisms to model the context of candidate news and capture the relationship between each news article and other news articles in the candidate list.By crossing the candidate news features with user features,we further capture the relevance of news articles.We use list-pairwise samples to train a ranking score that considers diversity and relevance,and we sort the candidate news list in descending order based on the score to obtain the final recommendation list that balances both accuracy and diversity.Compared with heuristic algorithms and learning algorithms based on sequential selection,our model has better computing performance and can better capture the context of the candidate list,effectively balancing the diversity and accuracy of recommendation.(4)Based on the proposed recommendation models and algorithms,we design and implement a news recommendation system that meets various needs of different users.The personalized news recommendation model based on content and recency provides accurate personalized news recommendation services,the news recommendation model based on popularity and user preferences provides a popular news list through popularity prediction algorithms,and integrating popularity can improve the recommendation performance for users with fewer browsing records.The context-aware diversified news recommendation model reorders the initial recommendation list to obtain a final recommendation list that balances both diversity and accuracy.