Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent factors.Recently,the item-item ...Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent factors.Recently,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation performance.However,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item embeddings.In this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance matrix.Specifically,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent space.We also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item embeddings.Experiments on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes sparser.By leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.展开更多
大多数推荐算法常采用基于迁移学习的跨领域推荐技术,借助辅助领域的丰富数据信息来解决传统单域推荐中普遍存在的数据稀疏等问题。但若迁移的知识比较单一,没有结合用户行为,则往往会在目标领域导致负迁移、推荐结果不佳等问题。因此,...大多数推荐算法常采用基于迁移学习的跨领域推荐技术,借助辅助领域的丰富数据信息来解决传统单域推荐中普遍存在的数据稀疏等问题。但若迁移的知识比较单一,没有结合用户行为,则往往会在目标领域导致负迁移、推荐结果不佳等问题。因此,考虑结合其他知识来辅助完成目标领域的学习任务。利用用户异构行为改善推荐结果,正是近年来的新兴研究热点之一。在用户数据中,标签与用户的真实偏好相关,通常能够反映用户或项目的部分隐式特征。通过结合迁移学习及用户标签数据,文中提出了基于标签迁移的跨领域项目推荐算法ITTCF(Item-based Tag Transfer Collaborative Filtering)。该算法摒弃了在跨领域迁移推荐中仅对评分模式进行挖掘迁移的单一辅助方式,将用户行为反馈与数字评分相结合,融合了评分模式和标签这两种异构用户行为。在多个数据集中的实验结果均表明,ITTCF具有更好的RMSE和MAE值,较传统算法分别提升了1.61%~6.67%和1.97%~8.83%。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61672322,61672324)the Natural Science Foundation of Shandong Province(2016ZRE27468)the Fundamental Research Funds of Shandong University.
文摘Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent factors.Recently,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation performance.However,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item embeddings.In this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance matrix.Specifically,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent space.We also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item embeddings.Experiments on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes sparser.By leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.
文摘大多数推荐算法常采用基于迁移学习的跨领域推荐技术,借助辅助领域的丰富数据信息来解决传统单域推荐中普遍存在的数据稀疏等问题。但若迁移的知识比较单一,没有结合用户行为,则往往会在目标领域导致负迁移、推荐结果不佳等问题。因此,考虑结合其他知识来辅助完成目标领域的学习任务。利用用户异构行为改善推荐结果,正是近年来的新兴研究热点之一。在用户数据中,标签与用户的真实偏好相关,通常能够反映用户或项目的部分隐式特征。通过结合迁移学习及用户标签数据,文中提出了基于标签迁移的跨领域项目推荐算法ITTCF(Item-based Tag Transfer Collaborative Filtering)。该算法摒弃了在跨领域迁移推荐中仅对评分模式进行挖掘迁移的单一辅助方式,将用户行为反馈与数字评分相结合,融合了评分模式和标签这两种异构用户行为。在多个数据集中的实验结果均表明,ITTCF具有更好的RMSE和MAE值,较传统算法分别提升了1.61%~6.67%和1.97%~8.83%。
