关键词: 时间敏感, 兴趣稳定性, 物品流行度, 协同过滤, 流行偏置

Abstract: In terms of the phenomenon of object popularity bias, a user interest feature similarity model based on item popularity is proposed by introducing object popularity into user interest modeling. What’s more, in terms of  the problem that the traditional model does not take the stability of user interest and the difficulty of capturing user interest in real time into consideration, a similarity model with time-sensitive and interest-stability is proposed, which is based on introducing time-sensitive and interest-stability during calculating user interest similarity. Finally, a novel IPSTS algorithm model is proposed by using the weighting factors to linearly weight the two similarity models. Experiments show that the algorithm can significantly reduce the Mean Absolute Error（MAE） and Root Mean Square Error（RMSE） compared with the traditional collaborative filtering algorithm, and can also excavate long tail items and relieve phenomenon of popularity bias.

Key words: time-sensitive, stability of interest, prevalence of item, collaborative filtering, popularity bias