Work

Post-Training Embedding Enhancement for Long-Tail Recommendation

Year: 2024

Type: conference-paper

Abstract: Item popularity in real-world data follows a long-tail distribution, where a few items attract most of the attention, while the majority receive much less. This disparity results in high-quality embeddings for popular (head) items, but lower-quality embeddings for unpopular (tail) items, leading to less accurate recommendations for the latter. Our observations confirm that embeddings of tail items often exhibit (1) magnitudes (i.e., norms) that are less reflective of actual popularity and (2) directions that are less effective in capturing user preferences, compared to those of head items.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3627673.3679978

Authors: Geon Lee, Kyungho Kim, Kijung Shin

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid