Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users’ historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model.

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After the outbreak of the Covid-19 epidemic, China has implemented a stay-at-home policy, and the rise of short videos led to a large number of businesses which started using short videos for marketing. The popularity of short videos during the epidemic has made us pay more attention to its marketing strategies. Among them, the marketing strategy of short videos utilize a lot of professional knowledge, such as the area in internet celebrity economy, vanity effect, herd effect and hunger marketing. In addition, short video marketing also uses a lot of technological expertise, such as the calculation of big data and the visualization of text. Also people get spiritual happiness while shopping. This study is going to use some prior findings and find a new way of outlook into the marketing strategy of short videos during the epidemic, and its future aspirations.