Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks

Wu, Peizhi; Tu, Yi; Yuan, Xiaojie; Jatowt, Adam; Yang, Zhenglu

doi:10.24963/ijcai.2018/226

Cited by 12 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social recommendation has emerged in these platforms, with the goal to model the social influence and social correlation among users to boost recommendation performance. The underlying reason for social recommendation is the existence of social influence among social neighbors, leading to the correlation of users' interests in a social network [145], [146], [147], [148], [149], [150], [151]. We summarize the social recommendation models into the following two categories: the social correlation enhancement and regularization models, and GNN based models.…”

Section: Modeling Social Networkmentioning

confidence: 99%

“…In the real-world, users' interests are dynamic over time due to users' personal interests change and the varying social influence strengths. Researchers extended the social correlation based model with RNN to model the evolution of users' preferences under dynamic social influences [146], [147]. Specifically, for each user u, her latent preferences h t a at time t could be modeled as the transition from her previous latent preference h t−1 u , as well as the social influence from social neighbors at t − 1 as:…”

Section: Modeling Social Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize the field to facilitate future progress. Distinct from existing surveys that categorize existing methods based on the taxonomy of deep learning techniques, we instead summarize the field from the perspective of recommendation modeling, which could be more instructive to researchers and practitioners working on recommender systems. Specifically, we divide the work into three types based on the data they used for recommendation modeling: 1) collaborative filtering models, which leverage the key source of user-item interaction data; 2) content enriched models, which additionally utilize the side information associated with users and items, like user profile and item knowledge graph; and 3) context enriched models, which account for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative works for each type, we finally discuss some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.

show abstract

Section: Modeling Social Networkmentioning

confidence: 99%

Section: Modeling Social Networkmentioning

confidence: 99%

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recommendation. In social recommendation, we have social networks that contain the social relations of each user, and the goal is to utilize the local neighbors' preferences for each user in social networks to enhance the user modeling [17,138,165,166]. From the perspective of representation learning with GNN, there are two key considerations in social recommendation: 1) how to capture the social factor; 2) how to combine the social factor from friends and user preference from his/her interaction behaviors.…”

Section: Gnn In Socialmentioning

confidence: 99%

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Chen¹,

Zheng²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach of recommender systems. In this survey, we conduct a comprehensive review of the literature in graph neural network-based recommender systems. We first introduce the background and the history of the development of both recommender systems and graph neural networks. For recommender systems, in general, there are four aspects for categorizing existing works: stage, scenario, objective, and application. For graph neural networks, the existing methods consist of two categories, spectral models and spatial ones. We then discuss the motivation of applying graph neural networks into recommender systems, mainly consisting of the high-order connectivity, the structural property of data, and the enhanced supervision signal. We then systematically analyze the challenges in graph construction, embedding propagation/aggregation, model optimization, and computation efficiency. Afterward and primarily, we provide a comprehensive overview of a multitude of existing works of graph neural network-based recommender systems, following the taxonomy above. Finally, we raise discussions on the open problems and promising future directions of this area. We summarize the representative papers along with their codes repositories in https://github.com/tsinghua-fib-lab/GNN-Recommender-Systems.

show abstract

“…However, the complicated social effects occurring amongst viewers watching common channels are not carefully examined. On the other hand, existing social-aware recommendations [30,32] only infer personal satisfaction based on social topology, which is not designed for MSP where viewers watch live streaming channels together. Hence, we parameterize MSP personal satisfaction from personal, social, and streamer relation aspects, to design a new ranking model CARS to quantify the satisfaction of different combinations of channels and friends.…”

Section: Research Challengesmentioning

confidence: 99%

“…6.1.3 Evaluation Metrics. In Section 6.2, the evaluation on donation recommendations is based on Root-Mean-Square Error (RMSE) [32,34]. For tensor factorization, we evaluate the average reconstruction loss with respect to donation and response tensors.…”

Section: Channel Influence-aware Msp Ranking System (Cars)mentioning

confidence: 99%

Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

Lai

Tsai

Shuai

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via sociotemporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.

show abstract

Neural Framework for Joint Evolution Modeling of User Feedback and Social Links in Dynamic Social Networks

Cited by 12 publications

References 0 publications

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization

Contact Info

Product

Resources

About