Temporal Meta-path Guided Explainable Recommendation

Chen, Hongxu; Li, Yicong; Sun, Xiangguo; Xu, Guandong; Yin, Hongzhi

doi:10.1145/3437963.3441762

Cited by 73 publications

(33 citation statements)

References 34 publications

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“…Meanwhile, concerns about the nature and operation of the deep neural network's black box have grown, driving an increase in curiosity in deconstructing its essential components and understanding its functions. Therefore, explainability has lately received a lot of attention, owing to the requirement to explain the internal mechanics of a deep learning system [5,63]. Many recent studies have focused on improving the transparency of deep neural networks to be adequately understood and be reliable.…”

Section: Explainable Deep Learningmentioning

confidence: 99%

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

et al. 2022

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The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

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Section: Explainable Deep Learningmentioning

confidence: 99%

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

et al. 2022

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“…There are also solutions exploiting other types of information for explainable recommendation, such as item-item relation (Chen et al, 2021), knowledge graph (Xian et al, 2019) and social network (Ji and Shen, 2016). But they are clearly beyond the scope of this work.…”

Section: Related Workmentioning

confidence: 99%

Comparative Explanations of Recommendations

Yang,

Wang,

Cai

et al. 2021

Preprint

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As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system's. Unfortunately, little research attention has yet been paid on such comparative explanations.In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution.

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“…First, to initialize users and items, we use DeepWalk [10] to embed user and item entities. Secondly, instead of pre-defined meta-paths and randomly generating metapaths instances [6], we utilize a reinforcement learning with a Markov Decision Process (MDP) environment to explore useful and meaningful sequential (temporal) and non-sequential paths to improve the recommendation performance and personalization. In this step, we obtain itemitem instance paths between consecutive items using reinforcement learning framework.…”

Section: Overview Of Tmer-rl Architecturementioning

confidence: 99%

“…Our previous work [6] pre-defines use-item and item-item meta-paths on the recommendation knowledge graph, and randomly selects meta-path instances from all existing ones. The hand-crafted meta-paths not only need human efforts, but also are difficult to be determined when dealing with large recommendation knowledge graphs.…”

Section: Reinforcement Learning For Paths Explorationmentioning

confidence: 99%

“…It is time-consuming for a model to select several paths that are meaningful, expressive, and have positive impacts on both recommendation performance and explainability. To this end, inspired by prior work [5], our previous work TMER [6] leverages the concept of meta-path [7] and explore diverse meta-path schemas to characterize the context of dynamic interactions between users and items. However, it has to pre-define meta-paths and randomly sample path instances from all generated ones, which involves human and random factor in the following recommendation module.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Li¹,

Chen²,

Li³

et al. 2021

Preprint

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Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.

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Temporal Meta-path Guided Explainable Recommendation

Cited by 73 publications

References 34 publications

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Comparative Explanations of Recommendations

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

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