Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
This addresses the cold-start issue for new users and items in recommendation systems, though it is incremental as it builds on existing KG-aware methods.
The paper tackles the cold-start problem in recommendation systems by using pseudo-labelling over a knowledge graph to leverage unobserved user-item pairs as positive instances, achieving improvements over state-of-the-art KG-aware recommenders, especially for cold-start users and items.
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent users' information needs. Most present works leverage unobserved samples for extracting negative signals. However, such an optimisation strategy can lead to biased results toward already popular items by frequently handling new items as negative instances. In this study, we tackle the cold-start problems for new users/items by appropriately leveraging unobserved samples. We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling. Our approach aggressively employs unobserved samples as positive instances and brings new items into the spotlight. To avoid exhaustive label assignments to all possible pairs of users and items, we exploit a KG for selecting probably positive items for each user. We also utilise an improved negative sampling strategy and thereby suppress the exacerbation of popularity biases. Through experiments, we demonstrate that our approach achieves improvements over the state-of-the-art KG-aware recommenders in a variety of scenarios; in particular, our methodology successfully improves recommendation performance for cold-start users/items.