Collaborative filtering problems are commonly solved based on matrix
completion techniques which recover the missing values of user-item interaction
matrices. In a matrix, the rating position specifically represents the user
given and the item rated. Previous matrix completion techniques tend to neglect
the position of each element (user, item and ratings) in the matrix but mainly
focus on semantic similarity between users and items to predict the missing
value in a matrix. This paper proposes a novel position-enhanced user/item
representation training model for recommendation, SUPER-Rec. We first capture
the rating position in the matrix using the relative positional rating encoding
and store the position-enhanced rating information and its user-item
relationship to the fixed dimension of embedding that is not affected by the
matrix size. Then, we apply the trained position-enhanced user and item
representations to the simplest traditional machine learning models to
highlight the pure novelty of our representation learning model. We contribute
the first formal introduction and quantitative analysis of position-enhanced
item representation in the recommendation domain and produce a principled
discussion about our SUPER-Rec to the outperformed performance of typical
collaborative filtering recommendation tasks with both explicit and implicit
feedback.

本研究提出了一种新的位置增强的用户 / 物品表示训练模型 SUPER-Rec，通过使用相对位置评分编码来捕获用户评分矩阵中的评分位置并将其与不受矩阵大小影响的嵌入固定维度一起存储，从而为推荐任务提供更好的表现。

超级推荐：增强环境位置表示的推荐模型

SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation

Learning informative representations of users and items from the interaction
data is of crucial importance to collaborative filtering (CF). Present
embedding functions exploit user-item relationships to enrich the
representations, evolving from a single user-item instance to the holistic
interaction graph. Nevertheless, they largely model the relationships in a
uniform manner, while neglecting the diversity of user intents on adopting the
items, which could be to pass time, for interest, or shopping for others like
families. Such uniform approach to model user interests easily results in
suboptimal representations, failing to model diverse relationships and
disentangle user intents in representations.
In this work, we pay special attention to user-item relationships at the
finer granularity of user intents. We hence devise a new model, Disentangled
Graph Collaborative Filtering (DGCF), to disentangle these factors and yield
disentangled representations. Specifically, by modeling a distribution over
intents for each user-item interaction, we iteratively refine the intent-aware
interaction graphs and representations. Meanwhile, we encourage independence of
different intents. This leads to disentangled representations, effectively
distilling information pertinent to each intent. We conduct extensive
experiments on three benchmark datasets, and DGCF achieves significant
improvements over several state-of-the-art models like NGCF, DisenGCN, and
MacridVAE. Further analyses offer insights into the advantages of DGCF on the
disentanglement of user intents and interpretability of representations. Our
codes are available in
this https URL

该研究提出了一种基于图模型的新方法 ——Disentangled Graph Collaborative Filtering（DGCF），用于在用户意图的更细粒度的层次上进行用户 - 物品关系建模和表示学习，以解决现有协同过滤方法中用户兴趣多样性建模的问题，并实现优于几种最先进的模型的结果。