基于SLIM的优势,本文提出的GLSLIM从global联系local的角度阐述了一个新的model,具体思路如下:
每个用户alpha不一样:
每个用户在训练过程中,都有自己的error loss, 将其求导并等于0后即可以得到alpha的表达式, 所以每个用户的alpha不一样
动态更新每个用户所属的local:
实际上是将该用户放到每个local中并算出对应的error loss, error loss最小的即为right local
所以对比实验就有: global-item-update; global-item-noupdate; global-update; global-noupdate(SLIM); local-update
其它对比算法: BPR; pureSVD; SLIM
进一步了解: ref https://mp.weixin.qq.com/s/LnV-Oq3pCCeMk9RRhha-Aw
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Local Item-Item Models for Top-N Recommendation (GLSLIM) | |||||||||||
| 文献作者 | Evangelia Christakopoulou and George Karypis | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| RecSys 2016 best paper; SLIM; global-local;GLSLIM | |||||||||||
| 摘要描述 | |||||||||||
| Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way – instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local item-item models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches. | |||||||||||
