Neural Collaborative Reasoning

Chen, Hanxiong; Shi, Shaoyun; Li, Yunqi; Zhang, Yongfeng

doi:10.1145/3442381.3449973

Cited by 67 publications

(38 citation statements)

References 41 publications

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“…the premise), but when creating the training examples, the target item (i.e., the consequent) could be an interaction that happened before the items in the premise, which forces the model to use future events to predict previous events. This violates the model assumption and thus results in scarified performance, which is consistent with the observations in [5]. On ML100k and Amazon Baby, where the sequential and reasoning models can be properly executed, we see that both sequential and reasoning models are better than matching models.…”

Section: Overall Performancesupporting

confidence: 82%

“…• GRU4Rec[11]: A session-based recommendation model, which uses recurrent neural networks-in particular, Gated Recurrent Units (GRU)-to capture sequential patterns.• STAMP[26]: The Short-Term Attention/Memory Priority model, which uses the attention mechanism to model both short-term and long-term user preferences.• NLR[39]: A reasoning-based model, which adopts Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning for recommendation. • NCR[5]: The Neural Collaborative Reasoning model, which organizes the logic expressions as neural networks for reasoning and prediction in a continuous space.…”

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confidence: 99%

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Causal Collaborative Filtering

Xu,

Ge,

et al. 2021

Preprint

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Recommender systems are important and valuable tools for many personalized services. Collaborative Filtering (CF) algorithms-among others-are fundamental algorithms driving the underlying mechanism of personalized recommendation. Many of the traditional CF algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data for matching, including memory-based methods such as user/item-based CF as well as learning-based methods such as matrix factorization and deep learning models. However, advancing from correlative learning to causal learning is an important problem, because causal/counterfactual modeling can help us to think outside of the observational data for user modeling and personalization. In this paper, we propose Causal Collaborative Filtering (CCF)-a general framework for modeling causality in collaborative filtering and recommendation. We first provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We then propose a conditional intervention approach for 𝑑𝑜-calculus so that we can estimate the causal relations based on observational data. Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. Experiments are conducted on two types of real-world datasets-traditional and randomized trial data-and results show that our framework can improve the recommendation performance of many CF algorithms.

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Section: Overall Performancesupporting

confidence: 82%

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confidence: 99%

Causal Collaborative Filtering

Xu,

Ge,

et al. 2021

Preprint

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“…Another track of research uses auto-encoder for recommendation [32,38], but they model the user history profiles instead of the distribution of slates. A recent line of research adopts reasoning-based recommendation models [11,40,47], which models recommendation as a cognition rather than perception task and adopts neural reasoning rather than neural matching models for better recommendation.…”

Section: Related Workmentioning

confidence: 99%

Variation Control and Evaluation for Generative Slate Recommendations

Liu

Sun

et al. 2021

Proceedings of the Web Conference 2021

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Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intralist positional biases and item relations. In order to deal with the enormous combinatorial space of slates, recent work considers a generative solution so that a slate distribution can be directly modeled. However, we observe that such approaches-despite their proved effectiveness in computer vision-suffer from a trade-off dilemma in recommender systems: when focusing on reconstruction, they easily over-fit the data and hardly generate satisfactory recommendations; on the other hand, when focusing on satisfying the user interests, they get trapped in a few items and fail to cover the item variation in slates. In this paper, we propose to enhance the accuracy-based evaluation with slate variation metrics to estimate the stochastic behavior of generative models. We illustrate that instead of reaching to one of the two undesirable extreme cases in the dilemma, a valid generative solution resides in a narrow "elbow" region in between. And we show that item perturbation can enforce slate variation and mitigate the over-concentration of generated slates, which expand the "elbow" performance to an easy-to-find region. We further propose to separate a pivot selection phase from the generation process so that the model can apply perturbation before generation. Empirical results show that this simple modification can provide even better variance with the same level of accuracy compared to post-generation perturbation methods. CCS CONCEPTS• Information systems → Recommender systems.

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“…Explainable AI has been an important topic in recommender systems [5,6,13,36,41,46,47], natural language processing [8,16,20] and computer vision [7,10,15,25,38]. To improve the transparency of deep neural networks, many explanation techniques have been proposed in recent years.…”

Section: Related Work 21 Explainability In Deep Learning and Aimentioning

confidence: 99%

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

Tan,

Geng,

et al. 2022

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Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the complex topology, it is difficult to process and make use of the rich information within such data. Graph Neural Networks (GNNs) have shown great advantages on learning representations for structural data. However, the non-transparency of the deep learning models makes it non-trivial to explain and interpret the predictions made by GNNs. Meanwhile, it is also a big challenge to evaluate the GNN explanations, since in many cases, the ground-truth explanations are unavailable.In this paper, we take insights of Counterfactual and Factual (CF 2 ) reasoning from causal inference theory, to solve both the learning and evaluation problems in explainable GNNs. For generating explanations, we propose a model-agnostic framework by formulating an optimization problem based on both of the two casual perspectives. This distinguishes CF 2 from previous explainable GNNs that only consider one of them. Another contribution of the work is the evaluation of GNN explanations. For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations. Experiments show that no matter ground-truth explanations are available or not, CF 2 generates better explanations than previous state-of-the-art methods on real-world datasets. Moreover, the statistic analysis justifies the correlation between the performance on ground-truth evaluation and our proposed metrics. CCS CONCEPTS• Computing methodologies → Machine learning; • Mathematics of computing → Computing most probable explanation; Graph algorithms.

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Neural Collaborative Reasoning

Cited by 67 publications

References 41 publications

Causal Collaborative Filtering

Causal Collaborative Filtering

Variation Control and Evaluation for Generative Slate Recommendations

Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning

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