Generating and Validating Contextually Relevant Justifications for Conversational Recommendation

Volokhin, Sergey; Collins, Marcus D.; Rokhlenko, Oleg; Agichtein, Eugene

doi:10.1145/3498366.3505789

Cited by 2 publications

(1 citation statement)

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“…Recommendation systems play a crucial role in addressing this issue. At present, recommendation systems have already become a common solution to provide information filtering and prediction in various domains, such as social networks and short videos [1][2][3]. The critical function of recommendation systems is to provide users with items they may find interesting while avoiding recommending items that they do not, thereby saving them time and effort while boosting the revenue of various companies.…”

Section: Introductionmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

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Section: Introductionmentioning

confidence: 99%