关键词: 隐语义模型, 隐向量嵌入, 隐式反馈, 融合模型, 推荐系统

Abstract:

In the information feeds consumption scenario, it is an essential problem to personalize content recommendation using the user’s implicit behavior feedback. Due to behavioral inertia, users usually only browse the feed stream, so the interactive behavior data is relatively sparse, which leads to the low performance of traditional methods, expecially in terms of personalization. Aiming at this problem, this paper designs the weight conversion method of implicit feedback, and then proposes the LFM-XGB-LR hybrid model. It uses the LFM algorithm to generate the embedded vector, and combines the advantages of XGB in feature crossing and LR in discrete calculation. Experiment and analysis results show that LFM embedding improves individuation of the recommendation, and the hybrid model has improvements on each evaluation index.

Key words: Latent Factor Model（LFM）, implicit vector embedding, implicit feedback, hybrid model, recommender system