  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.
