本次论文集合的方向主要包括可解释推荐算法[1,14]、基于多兴趣建模的推荐算法[2]、预测下次购买行为的序列推荐算法[3]、基于协作感知图卷积网络的推荐算法[4]、多模态推荐算法[5]、基于图划分的高效推荐算法[6]、面向医生推荐的联邦图注意力推荐算法[7]、多行为超图增强的序列推荐算法[9]、基于可信执行环境的推荐系统[10]、交互式推荐算法[11]、推荐系统的全面评估[13]等。

以下整理了论文标题以及摘要，如感兴趣可移步原文精读。

• 1. Reinforced Path Reasoning for Counterfactual Explainable Recommendation

• 2. Every Preference Changes Differently: Neural Multi-Interest Preference  Model with Temporal Dynamics for Recommendation
• 3. Sequential Recommendation Model for Next Purchase Prediction
• 4. Collaboration-Aware Graph Convolutional Networks for Recommendation  Systems
• 5. Bootstrap Latent Representations for Multi-modal Recommendation
• 6. Efficient and Scalable Recommendation via Item-Item Graph Partitioning
• 7. FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for  Doctor Recommendation Using EHR
• 8. Docent: A content-based recommendation system to discover contemporary  art
• 9. Multi-Behavior Hypergraph-Enhanced Transformer for Sequential  Recommendation
• 10. Horizontal Federated Learning and Secure Distributed Training for  Recommendation System with Intel SGX
• 11. Interactive Recommendations for Optimal Allocations in Markets with  Constraints
• 12. Developing an NLP-based Recommender System for the Ethical, Legal, and  Social Implications of Synthetic Biology
• 13. EvalRS: a Rounded Evaluation of Recommender Systems
• 14. On the Relationship between Counterfactual Explainer and Recommender: A  Framework and Preliminary Observations

1. Reinforced Path Reasoning for Counterfactual Explainable Recommendation

Xiangmeng Wang, Qian Li, Dianer Yu, Guandong Xu

https://arxiv.org/abs/2207.06674

Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfactual explainable approaches face huge search space and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe item attribute-based explanations are more intuitive and persuadable for users since they explain by fine-grained item demographic features (e.g., brand). Moreover, counterfactual explanation could enhance recommendations by filtering out negative items.

In this work, we propose a novel Counterfactual Explainable Recommendation (CERec) to generate item attribute-based counterfactual explanations meanwhile to boost recommendation performance. Our CERec optimizes an explanation policy upon uniformly searching candidate counterfactuals within a reinforcement learning environment. We reduce the huge search space with an adaptive path sampler by using rich context information of a given knowledge graph. We also deploy the explanation policy to a recommendation model to enhance the recommendation. Extensive explainability and recommendation evaluations demonstrate CERec's ability to provide explanations consistent with user preferences and maintain improved recommendations. We release our code at below:

2. Every Preference Changes Differently: Neural Multi-Interest Preference  Model with Temporal Dynamics for Recommendation

Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, Jishen Zhao

https://arxiv.org/abs/2207.06652

User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems as well. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user's interest in a certain topic. With multi-interest representation, it's important to model the user's preference over the different topics and how the preference change with time. However, existing approaches either fail to estimate the user's affinity to each interest or unreasonably assume every interest of every user fades with an equal rate with time, thus hurting the recall of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.

3. Sequential Recommendation Model for Next Purchase Prediction

Xin Chen, Alex Reibman, Sanjay Arora

https://arxiv.org/abs/2207.06225

Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers' general preferences rather than a customer's specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user's current needs but also better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The method first employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-time predictions using the sequential RS into Nexus, a scalable, low-latency, event-based digital experience architecture.

4. Collaboration-Aware Graph Convolutional Networks for Recommendation  Systems

Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr

https://arxiv.org/abs/2207.06221

By virtue of the message-passing that implicitly injects collaborative effect into the embedding process, Graph Neural Networks (GNNs) have been successfully adopted in recommendation systems. Nevertheless, most of existing message-passing mechanisms in recommendation are directly inherited from GNNs without any recommendation-tailored modification. Although some efforts have been made towards simplifying GNNs to improve the performance/efficiency of recommendation, no study has comprehensively scrutinized how message-passing captures collaborative effect and whether the captured effect would benefit the prediction of user preferences over items. Therefore, in this work we aim to demystify the collaborative effect captured by message-passing in GNNs and develop new insights towards customizing message-passing for recommendation. First, we theoretically analyze how message-passing captures and leverages the collaborative effect in predicting user preferences. Then, to determine whether the captured collaborative effect would benefit the prediction of user preferences, we propose a recommendation-oriented topological metric, Common Interacted Ratio (CIR), which measures the level of interaction between a specific neighbor of a node with the rest of its neighborhood set. Inspired by our theoretical and empirical analysis, we propose a recommendation-tailored GNN, Augmented Collaboration-Aware Graph Convolutional Network (CAGCN*), that extends upon the LightGCN framework and is able to selectively pass information of neighbors based on their CIR via the Collaboration-Aware Graph Convolution. Experimental results on six benchmark datasets show that CAGCN* outperforms the most representative GNN-based recommendation model, LightGCN, by 9% in Recall@20 and also achieves more than 79% speedup. Our code is publicly available at below:

https://github.com/YuWVandy/CAGCN

5. Bootstrap Latent Representations for Multi-modal Recommendation

Xin Zhou, Hongyu Zhou, Yong Liu, Zhiwei Zeng, Chunyan Miao, Pengwei Wang, Yuan You, Feijun Jiang

https://arxiv.org/abs/2207.05969

This paper studies the multi-modal recommendation problem, where the item multi-modality information (eg. images and textual descriptions) is exploited to improve the recommendation accuracy. Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (eg. user-user or item-item relation graph) to augment the learned representations of users and/or items. These representations are often propagated and aggregated on auxiliary graphs using graph convolutional networks, which can be prohibitively expensive in computation and memory, especially for large graphs. Moreover, existing multi-modal recommendation methods usually leverage randomly sampled negative examples in Bayesian Personalized Ranking (BPR) loss to guide the learning of user/item representations, which increases the computational cost on large graphs and may also bring noisy supervision signals into the training process. To tackle the above issues, we propose a novel self-supervised multi-modal recommendation model, dubbed BM3, which requires neither augmentations from auxiliary graphs nor negative samples. Specifically, BM3 first bootstraps latent contrastive views from the representations of users and items with a simple dropout augmentation. It then jointly optimizes three multi-modal objectives to learn the representations of users and items by reconstructing the user-item interaction graph and aligning modality features under both inter- and intra-modality perspectives. BM3 alleviates both the need for contrasting with negative examples and the complex graph augmentation from an additional target network for contrastive view generation. We show BM3 outperforms prior recommendation models on three datasets with number of nodes ranging from 20K to 200K, while achieving a 2-9X reduction in training time. Our code is available at below:

https://github.com/enoche/BM3

6. Efficient and Scalable Recommendation via Item-Item Graph Partitioning

Tianjun Wei, Jianghong Ma, Tommy W. S. Chow

https://arxiv.org/abs/2207.05959

Collaborative filtering (CF) is a widely searched problem in recommender systems. Linear autoencoder is a kind of well-established method for CF, which estimates item-item relations through encoding user-item interactions. Despite the excellent performance of linear autoencoders, the rapidly increasing computational and storage costs caused by the growing number of items limit their scalabilities in large-scale real-world scenarios. Recently, graph-based approaches have achieved success on CF with high scalability, and have been shown to have commonalities with linear autoencoders in user-item interaction modeling. Motivated by this, we propose an efficient and scalable recommendation via item-item graph partitioning (ERGP), aiming to address the limitations of linear autoencoders. In particular, a recursive graph partitioning strategy is proposed to ensure that the item set is divided into several partitions of finite size. Linear autoencoders encode user-item interactions within partitions while preserving global information across the entire item set. This allows ERGP to have guaranteed efficiency and high scalability when the number of items increases. Experiments conducted on 3 public datasets and 3 open benchmarking datasets demonstrate the effectiveness of ERGP, which outperforms state-of-the-art models with lower training time and storage costs.

7. FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for  Doctor Recommendation Using EHR

Luning Bi, Yunlong Wang, Fan Zhang, Zhuqing Liu, Yong Cai, Emily Zhao

https://arxiv.org/abs/2207.05750

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these data, various doctor recommendation systems have been proposed. Typically, such studies process the EHR data in a flat-structured manner, where each encounter was treated as an unordered set of features. Nevertheless, the heterogeneous structured information such as service sequence stored in claims shall not be ignored. This paper presents a doctor recommendation system with time embedding to reconstruct the potential connections between patients and doctors using heterogeneous graph attention network. Besides, to address the privacy issue of patient data sharing crossing hospitals, a federated decentralized learning method based on a minimization optimization model is also proposed. The graph-based recommendation system has been validated on a EHR dataset. Compared to baseline models, the proposed method improves the AUC by up to 6.2%. And our proposed federated-based algorithm not only yields the fictitious fusion center's performance but also enjoys a convergence rate of O(1/T).

8. Docent: A content-based recommendation system to discover contemporary  art

Antoine Fosset, Mohamed El-Mennaoui, Amine Rebei, Paul Calligaro, Elise Farge Di Maria, Hélène Nguyen-Ban, Francesca Rea, Marie-Charlotte Vallade, Elisabetta Vitullo, Christophe Zhang, Guillaume Charpiat, Mathieu Rosenbaum

https://arxiv.org/abs/2207.05648

Recommendation systems have been widely used in various domains such as music, films, e-shopping etc. After mostly avoiding digitization, the art world has recently reached a technological turning point due to the pandemic, making online sales grow significantly as well as providing quantitative online data about artists and artworks. In this work, we present a content-based recommendation system on contemporary art relying on images of artworks and contextual metadata of artists. We gathered and annotated artworks with advanced and art-specific information to create a completely unique database that was used to train our models. With this information, we built a proximity graph between artworks. Similarly, we used NLP techniques to characterize the practices of the artists and we extracted information from exhibitions and other event history to create a proximity graph between artists. The power of graph analysis enables us to provide an artwork recommendation system based on a combination of visual and contextual information from artworks and artists. After an assessment by a team of art specialists, we get an average final rating of 75% of meaningful artworks when compared to their professional evaluations.

9. Multi-Behavior Hypergraph-Enhanced Transformer for Sequential  Recommendation

Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li

KDD 2022

https://arxiv.org/abs/2207.05584

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally, we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of-the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at:

https://github.com/yuh-yang/MBHT-KDD22

10. Horizontal Federated Learning and Secure Distributed Training for  Recommendation System with Intel SGX

Siyuan Hui, Yuqiu Zhang, Albert Hu, Edmund Song

https://arxiv.org/abs/2207.05079

With the advent of big data era and the development of artificial intelligence and other technologies, data security and privacy protection have become more important. Recommendation systems have many applications in our society, but the model construction of recommendation systems is often inseparable from users' data. Especially for deep learning-based recommendation systems, due to the complexity of the model and the characteristics of deep learning itself, its training process not only requires long training time and abundant computational resources but also needs to use a large amount of user data, which poses a considerable challenge in terms of data security and privacy protection. How to train a distributed recommendation system while ensuring data security has become an urgent problem to be solved. In this paper, we implement two schemes, Horizontal Federated Learning and Secure Distributed Training, based on Intel SGX(Software Guard Extensions), an implementation of a trusted execution environment, and TensorFlow framework, to achieve secure, distributed recommendation system-based learning schemes in different scenarios. We experiment on the classical Deep Learning Recommendation Model (DLRM), which is a neural network-based machine learning model designed for personalization and recommendation, and the results show that our implementation introduces approximately no loss in model performance. The training speed is within acceptable limits.

11. Interactive Recommendations for Optimal Allocations in Markets with  Constraints

Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran

https://arxiv.org/abs/2207.04143

Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most. Despite the prevalence of capacity constraints on allocations in many real-world recommendation settings, a principled way of incorporating them in the design of these systems has been lacking. Motivated by this, we propose an interactive framework where the system provider can enhance the quality of recommendations to the users by opportunistically exploring allocations that maximize user rewards and respect the capacity constraints using appropriate pricing mechanisms. We model the problem as an instance of a low-rank combinatorial multi-armed bandit problem with selection constraints on the arms. We employ an integrated approach using techniques from collaborative filtering, combinatorial bandits, and optimal resource allocation to provide an algorithm that provably achieves sub-linear regret, namely in rounds for a problem with users, items and rank mean reward matrix. Empirical studies on synthetic and real-world data also demonstrate the effectiveness and performance of our approach.

12. Developing an NLP-based Recommender System for the Ethical, Legal, and  Social Implications of Synthetic Biology

Damien Dablain, Lilian Huang, Brandon Sepulvado

https://arxiv.org/abs/2207.06360

Synthetic biology is an emerging field that involves the engineering and re-design of organisms for purposes such as food security, health, and environmental protection. As such, it poses numerous ethical, legal, and social implications (ELSI) for researchers and policy makers. Various efforts to ensure socially responsible synthetic biology are underway. Policy making is one regulatory avenue, and other initiatives have sought to embed social scientists and ethicists on synthetic biology projects. However, given the nascency of synthetic biology, the number of heterogeneous domains it spans, and the open nature of many ethical questions, it has proven challenging to establish widespread concrete policies, and including social scientists and ethicists on synthetic biology teams has met with mixed success.

This text proposes a different approach, asking instead is it possible to develop a well-performing recommender model based upon natural language processing (NLP) to connect synthetic biologists with information on the ELSI of their specific research? This recommender was developed as part of a larger project building a Synthetic Biology Knowledge System (SBKS) to accelerate discovery and exploration of the synthetic biology design space. Our approach aims to distill for synthetic biologists relevant ethical and social scientific information and embed it into synthetic biology research workflows.

13. EvalRS: a Rounded Evaluation of Recommender Systems

Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

CIKM 2022 Data Challenge Paper

https://arxiv.org/abs/2207.05772

Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces. However, research focused almost exclusively on the ability of RSs to produce accurate item rankings while giving little attention to the evaluation of RS behavior in real-world scenarios. Such narrow focus has limited the capacity of RSs to have a lasting impact in the real world and makes them vulnerable to undesired behavior, such as reinforcing data biases. We propose EvalRS as a new type of challenge, in order to foster this discussion among practitioners and build in the open new methodologies for testing RSs "in the wild".

14. On the Relationship between Counterfactual Explainer and Recommender: A  Framework and Preliminary Observations

Gang Liu, Zhihan Zhang, Zheng Ning, Meng Jiang

KDD 2022 Workshop

https://arxiv.org/abs/2207.04317

Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the complexity and lack of explainability of the models. To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result. In this work, we present a general framework for both DNN and non-DNN models so that the counterfactual explainers all belong to it with specific choices of components. This framework first estimates the influence of a certain historical action after its removal and then uses search algorithms to find the minimal set of such actions for the counterfactual explanation. With this framework, we are able to investigate the relationship between the explainers and recommenders. We empirically study two recommender models (NCF and Factorization Machine) and two datasets (MovieLens and Yelp). We analyze the relationship between the performance of the recommender and the quality of the explainer. We observe that with standard evaluation metrics, the explainers deliver worse performance when the recommendations are more accurate. This indicates that having good explanations to correct predictions is harder than having them to wrong predictions. The community needs more fine-grained evaluation metrics to measure the quality of counterfactual explanations to recommender systems.

由于公众号试行乱序推送，您可能不再准时收到机器学习与推荐算法的推送。为了第一时间收到本号的干货内容， 请将本号设为星标，以及常点文末右下角的“在看”。