Research on new media recommendation algorithm based on comment text

Abstract

There are two major problems with traditional recommendation models: firstly, when user preferences change, the recommendation model does not change accordingly; Secondly, the limited ability to handle abnormal data leads to a lack of diversity in recommendation results due to the singularity of recommendation results. User behavior data has been a hot topic in the field of recommendation research in recent years, and it can be used to mine deeper user information. To extract user preferences from comment text and user source information, a UICTM model is proposed. Based on this, LDA technology is used to obtain the UIFT algorithm, which integrates time factors to optimize the UIFT algorithm. CF, TMF UIFT+was used for experimental data analysis and comparison. The results showed that the UIFT+algorithm outperformed other recommendation algorithms overall in terms of mean square error (MSE) and recommendation degree (ACC).