Explainable Reasoning over Knowledge Graphs for Recommendation

Wang, Xiang; Wang, Dingxian; Xu, C.; He, Xiangnan; Cao, Yixin; Chua, Tat-Seng

doi:10.1609/aaai.v33i01.33015329

Cited by 629 publications

(397 citation statements)

References 2 publications

Supporting

Mentioning

364

Contrasting

Unclassified

Order By: Relevance

“…For example, MF [20,26] projects the ID of each user and item as an embedding vector, and conducts inner product between them to predict an interaction. To enhance the embedding function, much effort has been devoted to incorporate side information like item content [2,30], social relations [33], item relations [36], user reviews [3], and external knowledge graph [31,34]. While inner product can force user and item embeddings of an observed interaction close to each other, its linearity makes it insufficient to reveal the complex and nonlinear relationships between users and items [14,15].…”

Section: Model-based Cf Methodsmentioning

confidence: 99%

Neural Graph Collaborative Filtering

Xiang

Wang

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

2,522

1,534

View full text Add to dashboard Cite

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect.In this work, we propose to integrate the user-item interactionsmore specifically the bipartite graph structure -into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the useritem graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in useritem graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec [39] and Collaborative Memory Network [5]. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/ xiangwang1223/neural_graph_collaborative_filtering. CCS CONCEPTS• Information systems → Recommender systems.

show abstract

Section: Model-based Cf Methodsmentioning

confidence: 99%

Neural Graph Collaborative Filtering

Xiang

Wang

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

2,522

1,534

View full text Add to dashboard Cite

show abstract

“…the corresponding items have been chosen. Wang et al [28] proposed an RNN based model to reason over KGs for recommendation. However, it requires enumerating all the possible paths between each user-item pair for model training and prediction, which can be impractical for large-scale knowledge graphs.…”

Section: Recommendation With Knowledge Graphsmentioning

confidence: 99%

“…However, the approach has difficulty in coping with numerous types of relations and entities in large real-world KGs, and hence it is incapable of exploring relationships between unconnected entities. Wang et al [28] first developed a path embedding approach for recommendation over KGs that enumerates all the qualified paths between every user-item pair, and then trained a sequential RNN model from the extracted paths to predict the ranking score for the pairs. The recommendation performance is further improved, but it is not practical to fully explore all the paths for each user-item pair in large-scale KGs.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Xian

Muthukrishnan

et al. 2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

405

249

View full text Add to dashboard Cite

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

show abstract

“…Automatic Knowledge Extraction. In the past few years, researchers from the natural language processing (NLP), data mining, and computer vision communities have conducted extensive studies on automatic knowledge extraction and its applications [28,29].…”

Section: Related Workmentioning

confidence: 99%

Who, Where, and What to Wear?

Yang

Liao

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

Self Cite

View full text Add to dashboard Cite

Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf tools due to their flexibility and satisfactory performance. For clothing recognition and occasion prediction, we unify the two tasks by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts. To alleviate the heavy burden of human annotations, we introduce a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data. In experiments, we contribute a benchmark dataset and conduct extensive experiments from both quantitative and qualitative perspectives. The results demonstrate the effectiveness of our model in fashion concept prediction, and the usefulness of extracted knowledge with comprehensive analysis. CCS CONCEPTS• Information systems → Specialized information retrieval.

show abstract

Explainable Reasoning over Knowledge Graphs for Recommendation

Cited by 629 publications

References 2 publications

Neural Graph Collaborative Filtering

Neural Graph Collaborative Filtering

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

Who, Where, and What to Wear?

Contact Info

Product

Resources

About