Since bundled recommendation can meet various demands of users in one stop, it has always been a research hotspot in the recommendation field. Recent methods usually construct bundle view and item view based on user-bundle interaction and user-item interaction information, and learn representations for users and bundles from these two views. However, they all ignore the affiliation information between bundles and items, which will lead to serious information loss. To address this, we propose MVCBRec, a novel Multi-View Contrastive learning Bundle Recommendation model. It first learns the comprehensive representations of users and bundles on three views by constructing user-bundle (U-B) graph, user-item (U-I) graph and bundle-item (B-I) graph respectively. Multi-view contrastive learning is then used to model the cooperative associations between the three views. Multi-view contrastive learning encourages the alignment of the same user/bundle between different views can extract complementary information, and increases the dispersion of different users/bundles can enhance the self-discrimination ability. Extensive experiments on three public datasets show that our method outperforms SOTA baseline by 4.04% ~ 21.14% on recall. In addition, various ablation studies are performed to unveil the mystery of the key components.

Based on research in cognitive psychology, humans typically seek a balance between their preference for familiar things and the exploration of new ones during decision-making. Therefore, studying the relationship between accuracy and diversity in recommendation systems is particularly meaningful. In recent years, recommender systems based on Graph Neural Networks (GNNs) have garnered significant attention for enhancing recommendation accuracy or diversity. However, existing works often improve accuracy or diversity at the expense of the other aspect, which is inconsistent with the complex needs of users. In this paper, we propose a novel Recommendation model that Balances Diversity and Accuracy with GNNs, called BDARec. Firstly, BDARec proposes a balanced neighborhood aggregation strategy to select diverse and accurate neighbor nodes for updating node embeddings in user-item bipartite heterogeneous graph. Secondly, to accelerate the convergence of BDARec, an enhanced category-boosted negative sampling strategy is proposed to select negative samples from the same category positive samples with a certain probability. Thirdly, we put forward a dynamic feature for each item to measure the importance of items in training phase. Finally, we conduct extensive experiments on three real-world datasets. Experimental results show that our model can even improve recall by 22.04%, hit ratio by 16.46%, and coverage by 10.27% when compared to the state-of-the-art comparison algorithm, which verifies that the proposed model can achieve the best balance between diversity and accuracy.

The surge of multimedia content-spanning images, audio, video, and text-on digital platforms has heightened the need for sophisticated bundle recommendation techniques to curate cohesive item sets that resonate with users' preferences amidst rich media environments. Bundle recommendation aims to recommend thematically related item sets to users based on their preferences by simulating their cognitive decision-making process. It not only enhances user experience but also significantly boosts merchant profits, making it an increasingly crucial research area. However, existing studies often face cognitive limitations in modeling user preferences. They typically adopt a unified rule to learn user and bundle representations from items, failing to capture the diversity of bundling strategies and the underlying factors of user preferences. Therefore, we propose a Category-Aware Bundle Recommendation model, called CABRec. Specifically, CABRec learns user preferences from three views: User-Bundle (UB), User-Item (UI) and Bundle-Item (BI). From the UB view, we model users' direct preferences for bundles based on user-bundle interactions. From the UI and BI views, we model users' indirect preferences for bundles by constructing item-bundle layer and item-user layer to learn category-aware representations of bundles and users, respectively. Then, to capture the preference variations of different users on different views, we propose a user-specific prediction layer to learn a set of personalized preference weights for each user. Finally, we apply contrastive learning across these three views to utilize the complementary information they provide, enabling the model to learn more robust and generalizable representations. Extensive experiments on three datasets demonstrate that our method surpasses the strongest baseline, achieving a 2.33% ~ 17.02% improvement on recall.