Enhanced Item Recommendation via Graph Properties in Sparse Data
20th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2024, Corfu, Greece, 27 - 30 June 2024, vol.714, pp.111-124, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 714
- Doi Number: 10.1007/978-3-031-63223-5_9
- City: Corfu
- Country: Greece
- Page Numbers: pp.111-124
- Keywords: bipartite networks, negative sampling, power-law distribution, Recommender system, sparsity
- Galatasaray University Affiliated: Yes
Abstract
Item recommendation for users is a salient feature of many transaction-based systems. Finding the most appropriate items is crucial for both marketing and analytical perspectives. The latest works focus on ranking-based personalized recommenders. However, they recommend the same number of items for everyone and still suffer from the interaction sparsity issue. We propose a complex-graph-oriented supervised learning-based link prediction with a realistic negative sampling application for overcoming these problems. We employ the power-law degree distribution property of the complex graphs to sample the negative instances. The experiments show that our method outperforms ranking-based personalized recommenders with a 20% increase in recommendation success in multiple evaluation metrics.