CoMix: Collaborative filtering with mixup for implicit datasets

Moon, Jaewan; Jeong, Yoonki; Chae, Dong-Kyu; Choi, Joon Weon; Shim, Hyunjung; Lee, Jongwuk

doi:10.1016/j.ins.2023.01.110

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…Belkhadir et al [13] proposed an intelligent recommendation method based on social regularization and trust information, which has high recommendation quality. Moon et al [14] proposed a collaborative filtering method for implicit datasets. The method can address the popularity bias and data sparsity problems.…”

Section: Related Workmentioning

confidence: 99%

APMDI-CF: An Effective and Efficient Recommendation Algorithm for Online Users

2023

KSII TIIS

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Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

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Section: Related Workmentioning

confidence: 99%

APMDI-CF: An Effective and Efficient Recommendation Algorithm for Online Users

2023

KSII TIIS

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“…This demonstrates the dominance of DM as a novel generative paradigm in multiple generation tasks. Looking back to the real-world recommender systems, it could be regarded as a generator of the complete user-item interaction matrix based on the extremely sparse supervised signals (Moon et al 2023). It prompts an intuitive question: Can we take full advantage of DM's potent generalization capability to generate user preferences on both observed and unobserved items, thereby addressing the sparsity issue in recommendation?…”

Section: Introductionmentioning

confidence: 99%

Plug-In Diffusion Model for Sequential Recommendation

Ma,

Xie,

Meng

et al. 2024

AAAI

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Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

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