Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Shameem A. Puthiya Parambath, Sanjay Chawla

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

Original languageEnglish
Pages (from-to)1560-1588
Number of pages29
JournalData Mining and Knowledge Discovery
Volume34
Issue number5
DOIs
StatePublished - Sep 1 2020
Externally publishedYes

Keywords

  • Item cold-start problem
  • Item recommendation
  • Recommender systems
  • Soft-cluster embeddings

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