A Discriminative-Based Geometric Deep Learning Model for Cross Domain Recommender Systems

Arthur, John Kingsley; Zhou, Chang; Mantey, Eric Appiah; Osei-Kwakye, Jeremiah; Chen, Yaru

doi:10.3390/app12105202

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…RS is used in various fields and many applications suffer from cold-start problem [14]- [16], [23], [24]. To address this problem, researchers have proposed various approaches [25].…”

Section: Related Workmentioning

confidence: 99%

A Flexible Two-Tower Model for Item Cold-Start Recommendation

Lee,

Cho

2023

IEEE Access

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One of the main challenges in recommendation system is the item cold-start problem, where absence of historical interactions or ratings in new items makes recommendation difficult. In order to solve the cold-start problem, hybrid neural network models using meta data of the item as a feature is widely used. However, existing cold-start models tend to focus too much on utilizing the side information of items, which may not be flexible enough to capture the interaction information of users. In this study, we propose a flexible framework for better capturing the interaction information of users. Specifically, we incorporate the multiple choice learning scheme into the two-tower neural network which is a popular recommendation model that consists of two towers -one for users and one for items. In our proposed framework, we construct two encoders. One of the two encoders, the tightly-coupled encoder, focuses on the side information of items with which the user has interacted, the other one, loosely-coupled encoder, focuses the user's interaction information. We utilize Gumbel-Softmax to stochastically select the encoder, enhancing the flexibility that considers not only item feature but also user interaction information. We evaluate our proposed framework on two datasets -the MLIMDb dataset which is a combination of widely used the MovieLens and IMDb datasets based on common movies, and the CiteULike dataset. The experimental results show that our proposed framework achieves state-of-the-art results on cold-start recommendation. In the Recall@150 experiments on the CiteULike dataset, we achieved improvement of approximately 2.7% compared to the base model. In the Recall@150 experiments on the MLIMDb dataset, we achieved improvement of approximately 5.2% compared to the base model. We further show our proposed model improves the performance in the warmstart settings. In the Recall@100 experiments on the Citeulike dataset, we observed an improvement of approximately 1.3% compared to the base model. In the Recall@100 experiments on the MLIMDb dataset, we observed an improvement of approximately 3.9% compared to the base model. Our proposed framework provides a flexible approach for capturing the diverse aspects of users in recommendation systems, even for cold-start items. As demonstrated through extensive experiments, our proposed model outperforms several state-of-the-art models on both datasets.

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“…RS is used in various fields and many applications suffer from cold-start problem [14]- [16], [23], [24]. To address this problem, researchers have proposed various approaches [25].…”

Section: Related Workmentioning

confidence: 99%

A Flexible Two-Tower Model for Item Cold-Start Recommendation

Lee,

Cho

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Thirdly, user behavior data is very sparse. Previous methods usually use only item prediction tasks to learn model parameters, thus it is often affected by data sparsity and cannot learn sequence representation well [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Long and Short-Term Interest Contrastive Learning Under Filter-Enhanced Sequential Recommendation

Yang

et al. 2023

IEEE Access

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Existing self-supervised sequential recommendations face the problem of noisy interactions and sparse sequence data, and train models based only on item prediction losses, so they usually fail to learn an appropriate sequential representation. We propose long and short-term interest contrastive learning under filter-enhanced sequential recommendation (FLSCSR). Specifically, a filtering algorithm is used on the user's interaction sequences to attenuate the noisy information in the sequence data. Two independent encoders are used to model the user's long-term and short-term interests separately on the filter-based enhanced interaction sequences. Then user-specific gating mechanisms are constructed to capture the longterm and short-term interests tailored to the user's personalized preferences, which are incorporated into the attention network to achieve better learning of interest representations in sequence recommendations. In addition, representation alignment learning goals are proposed to minimize the discrepancy between long-term and short-term interest representations in personalized global contexts and local sequence representations. Experiments were conducted on three public and industrial datasets, where the FLSCSR model could obtain superior performance compared to the benchmark model: AUC improves by 0.76%-2.02%, GAUC improves by 0.55%-1.01%, MRR improves by 1.19%-2.09%, and NDCG@2 improves by 1.07%-2.26%.

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