Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation

Huang, Xiaowen; Fang, Quan; Qian, Shengsheng; Sang, Jitao; Li, Yan; Xu, Changsheng

doi:10.1145/3343031.3350893

Cited by 87 publications

(63 citation statements)

References 19 publications

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“…a critical role in determining the recommendation, the model further predicts the importance of different values for that attribute. Huang et al (2019) further incorporated multi-modality knowledge graph for explainable sequential recommendation. Different from conventional item-level sequential modeling methods, the proposed method captured user dynamic preferences on user-item interaction-level by modeling the sequential interactions over knowledge graphs.…”

Section: Knowledge Graph-based Explainable Recommendationmentioning

confidence: 99%

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

464

241

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Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -especially model-based methods -have been proposed and applied in real-world systems.In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation.We also devote a section to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.Explainable recommendation refers to personalized recommendation algorithms that address the problem of why -they not only provide users or system designers with recommendation results, but also explanations to clarify why such items are recommended. In this way, it helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and user satisfaction of the recommendation systems. It also facilitates system designers to diagnose, debug, and refine the recommendation algorithm.To highlight the position of explainable recommendation in the recommender system research area, we classify personalized recommendation with a broad conceptual taxonomy. Specifically, personalized recommendation research can be classified into the 5W problemswhen, where, who, what, and why, corresponding to time-aware recommendation (when), location-based recommendation (where), social recommendation (who), application-aware re...

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Section: Knowledge Graph-based Explainable Recommendationmentioning

confidence: 99%

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

464

241

View full text Add to dashboard Cite

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“…Yang et al [49] build multiple graphs to improve the multimedia features. Huang et al [16] use a knowledge graph to enhance the explainable sequential recommender. Wang et al [46] recommend tags according to the social properties of users and tags.…”

Section: Related Work 51 Multimedia Recommendationmentioning

confidence: 99%

How to Learn Item Representation for Cold-Start Multimedia Recommendation?

Wang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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The ability of recommending cold items (that have no behavior history) is a core strength of multimedia recommendation compared with behavior-only collaborative filtering. To learn effective item representation, a key challenge lies in the discrepancy between training and testing, since the cold items only exist in the testing data. This means that the signal used to represent an item varies during training and testing-in the training stage, we can represent an item with both collaborative embedding and content embedding; whereas in the testing stage, we represent a cold item with content embedding only. Nevertheless, existing learning frameworks omit this critical discrepancy, resulting in suboptimal item representation for multimedia recommendation. In this work, we pay special attention to cold items in multimedia recommender training. To address the discrepancy, we use two vectors to represent an item: one follows the traditional way that combines collaborative embedding and content embedding, and the other assumes that the item is cold by replacing the collaborative embedding with zero vector. We then propose a Multi-Task Pairwise Ranking (MTPR) framework for model training, which enforces the observed interactions ranking higher than unobserved ones even if the item is assumed to be cold. As a general learning framework, Our MTPR is agnostic to the choice of the collaborative (and/or content) encoder. We demonstrate it on VBPR, a representative multimedia recommendation model based on matrix factorization. Extensive experiments on three datasets of diverse domains validate MTPR, which leads to better representation for both cold and non-cold items in the testing stage, thus improving the overall performance of multimedia recommendation.

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“…In this section, using the validation split, the metapaths to be used are chosen. In the literature, the knowledge of experts [2], heuristic approaches [7] or recently, algorithmic approaches [36], [61] are used for this purpose. Here, we consider the user-item paths as the building blocks and used UMU and MUM for MovieLens, UIU and IUI for Amazon, UBU, BUB for Yelp as the base metapaths.…”

Section: ) Parameter Tuningmentioning

confidence: 99%

MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks

et al. 2020

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Neural network-based recommendation algorithms have become the state-of-the-art in recommender systems and can achieve very high predictive accuracy. However, these models are usually considered as black boxes in terms of their interpretability due to the complex structure of their hidden layers. In this research work, we propose MP4Rec, a recommender system using heterogeneous information networks to provide both accurate and explainable recommendations. MP4Rec uses of user-user and item-item similarity matrices and applies a newly proposed pair-wise objective function to make top-N recommendations which are transparent and explainable. The similarity matrices are created from metapaths constructed with the PathSim algorithm, node embeddings with cosine similarity or their combinations. The proposed pair-wise objective function incorporates an additional soft constraint for pushing more explainable items into the top-N recommendations. We have performed several experiments that show the effectiveness of our model by outperforming the state-of-the-art and providing both accurate and explainable recommendations in three well-known datasets.

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Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation

Cited by 87 publications

References 19 publications

Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation: A Survey and New Perspectives

How to Learn Item Representation for Cold-Start Multimedia Recommendation?

MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks

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