Variational Session-based Recommendation Using Normalizing Flows

Zhang, Fan; Wen, Zijing; Zhang, Xiaoying; Trajcevski, Goce; Zhong, Ting

doi:10.1145/3308558.3313615

Cited by 16 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides using RNNs, some other neural networks have also be explored for the SBR task [6], [11], [35], [36], [37], [38], [39], [40], [41]. For example, Liu et al [6] propose the STAMP model that employs an attention network to emphasize the importance of the last click item when learning session representation.…”

Section: Neural Network Methodsmentioning

confidence: 99%

Graph Spring Network and Informative Anchor Selection for Session-based Recommendation

Zhang¹,

Wang²

2022

Preprint

View full text Add to dashboard Cite

Session-based recommendation (SBR) aims at predicting the next item for an ongoing anonymous session. The major challenge of SBR is how to capture richer relations in between items and learn ID-based item embeddings to capture such relations. Recent studies propose to first construct an item graph from sessions and employ a Graph Neural Network (GNN) to encode item embedding from the graph. Although such graph-based approaches have achieved performance improvements, their GNNs are not suitable for ID-based embedding learning for the SBR task. In this paper, we argue that the objective of such ID-based embedding learning is to capture a kind of neighborhood affinity in that the embedding of a node is similar to that of its neighbors' in the embedding space. We propose a new graph neural network, called Graph Spring Network (GSN), for learning ID-based item embedding on an item graph to optimize neighborhood affinity in the embedding space. Furthermore, we argue that even stacking multiple GNN layers may not be enough to encode potential relations for two item nodes far-apart in a graph. In this paper, we propose a strategy that first selects some informative item anchors and then encode items' potential relations to such anchors. In summary, we propose a GSN-IAS model (Graph Spring Network and Informative Anchor Selection) for the SBR task. We first construct an item graph to describe items' co-occurrences in all sessions. We design the GSN for ID-based item embedding learning and propose an item entropy measure to select informative anchors. We then design an unsupervised learning mechanism to encode items' relations to anchors. We next employ a shared gated recurrent unit (GRU) network to learn two session representations and make two next item predictions. Finally, we design an adaptive decision fusion strategy to fuse two predictions to make the final recommendation. Extensive experiments on three public datasets demonstrate the superiority of our GSN-IAS model over the state-of-the-art models.

show abstract

Section: Neural Network Methodsmentioning

confidence: 99%

Graph Spring Network and Informative Anchor Selection for Session-based Recommendation

Zhang¹,

Wang²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, we optimize the parameters to minimize the distance between the predicted probability and the ground-truth probability. For the bundle matching and generation tasks, we utilize multinomial likelihoods for distributions r u and x b as in previous works [14,[23][24][25][26][27][28], since it has shown more impressive results than other likelihoods such as Gaussian likelihood and logistic likelihood in top-k recommendation [14]. Thus, the losses are measured by KL-divergence between the observed probabilities and the predicted…”

Section: Multi-task Learning With Partially Shared Parametersmentioning

confidence: 99%

Accurate bundle matching and generation via multitask learning with partially shared parameters

2023

View full text Add to dashboard Cite

How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3× higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles.

show abstract

“…Hence, matrix factorization approaches have been successful in modeling users based on their interactions with items. Recent advances in probabilistic models, variational approaches, and deep factorization models have shown promising results regarding the recommendation tasks [14,19,22,28,30,38,49,51]. Mainly, they jointly learn user and item representations through optimizations for specific recommendation tasks.…”

Section: Related Workmentioning

confidence: 99%

Variational User Modeling with Slow and Fast Features

Fazelnia

Simon

Anderson

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

Recommender systems play a key role in helping users find their favorite music to play among an often extremely large catalog of items on online streaming services. To correctly identify users' interests, recommendation algorithms rely on past user behavior and feedback to aim at learning users' preferences through the logged interactions. User modeling is a fundamental part of this large-scale system as it enables the model to learn an optimal representation for each user. For instance, in music recommendation, the focus of this paper, users' interests at any time is shaped by their general preferences for music as well as their recent or momentary interests in a particular type of music. In this paper, we present a novel approach for learning user representation based on general and slow-changing user interests as well as fast-moving current preferences. We propose a variational autoencoder-based model that takes fast and slow-moving features and learns an optimal user representation. Our model, which we call FS-VAE, consists of sequential and non-sequential encoders to capture patterns in user-item interactions and learn users' representations. We evaluate FS-VAE on a real-world music streaming dataset. Our experimental results show a clear improvement in learning optimal representations compared to state-of-the-art baselines on the next item recommendation task. We also demonstrate how each of the model components, slow input feature, and fast ones play a role in achieving the best results in next item prediction and learning users' representations.

show abstract

Variational Session-based Recommendation Using Normalizing Flows

Cited by 16 publications

References 34 publications

Graph Spring Network and Informative Anchor Selection for Session-based Recommendation

Graph Spring Network and Informative Anchor Selection for Session-based Recommendation

Accurate bundle matching and generation via multitask learning with partially shared parameters

Variational User Modeling with Slow and Fast Features

Contact Info

Product

Resources

About