本文精选了上周（0313-0319）最新发布的18篇推荐系统相关论文。
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. Dually Enhanced Propensity Score Estimation in Sequential Recommendation, CIKM2022
2. Robust Preference-Guided Denoising for Graph based Social Recommendation
3. The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings, CHI2023
4. Disentangled Graph Social Recommendation, ICDE2023
5. Automated Self-Supervised Learning for Recommendation, WWW2023
6. CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
7. A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries
8. AutoDenoise: Automatic Data Instance Denoising for Recommendations
9. MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation
10. User Retention-oriented Recommendation with Decision Transformer
11. AutoMLP: Automated MLP for Sequential Recommendations, WWW2023
12. Semi-supervised Adversarial Learning for Complementary Item Recommendation, WWW2023
13. Fairness-aware Differentially Private Collaborative Filtering
14. Graph-less Collaborative Filtering, WWW2023
15. Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems, CHI2023
16. P-MMF: Provider Max-min Fairness Re-ranking in Recommender System, WWW2023
17. Vertical Federated Graph Neural Network for Recommender System
18. Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples
1. Dually Enhanced Propensity Score Estimation in Sequential Recommendation
Chen Xu, Jun Xu, Xu Chen, Zhenghua Dong, Ji-Rong Wen
https://arxiv.org/abs/2303.08722
Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-exposed to certain items. Unbiased learning based on inverse propensity scores (IPS), which estimate the probability of observing a user-item pair given the historical information, has been proposed to address the issue. In these methods, propensity score estimation is usually limited to the view of item, that is, treating the feedback data as sequences of items that interacted with the users. However, the feedback data can also be treated from the view of user, as the sequences of users that interact with the items. Moreover, the two views can jointly enhance the propensity score estimation. Inspired by the observation, we propose to estimate the propensity scores from the views of user and item, called Dually Enhanced Propensity Score Estimation (DEPS). Specifically, given a target user-item pair and the corresponding item and user interaction sequences, DEPS firstly constructs a time-aware causal graph to represent the user-item observational probability. According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data. Finally, two transformers are designed to make the final preference prediction. Theoretical analysis showed the unbiasedness and variance of DEPS. Experimental results on three publicly available and an industrial datasets demonstrated that DEPS can significantly outperform the state-of-the-art baselines.
2. Robust Preference-Guided Denoising for Graph based Social Recommendation
Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li
https://arxiv.org/abs/2303.08346
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving two state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at: https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec
3. The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings, CHI2023
Bereket A. Yilma, Luis A. Leiva
https://arxiv.org/abs/2303.08182
Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. In this paper, we focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for personalized recommendation. We propose and study recommender systems based on textual and visual feature learning techniques, as well as their combinations. We then perform a small-scale and a large-scale user-centric evaluation of the quality of the recommendations. Our results indicate that textual features compare favourably with visual ones, whereas a fusion of both captures the most suitable hidden semantic relationships for artwork recommendation. Ultimately, this paper contributes to our understanding of how to deliver content that suitably matches the user's interests and how they are perceived.
4. Disentangled Graph Social Recommendation, ICDE2023
Lianghao Xia, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, Jian Pei
https://arxiv.org/abs/2303.07810
Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN
5. Automated Self-Supervised Learning for Recommendation, WWW2023
Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao
https://arxiv.org/abs/2303.07797
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at: https://github.com/HKUDS/AutoCF
6. CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
Haonan Hu, Dazhong Rong, Jianhai Chen, Qinming He, Zhenguang Liu
https://arxiv.org/abs/2303.07607
The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their initial ID embeddings. However, we argue that the capability of these methods is limited, because they mainly utilize item attribute features which only contain little information, but ignore the useful collaborative information contained in the ID embeddings of users and old items. To tackle this issue, we propose CoMeta to enhance the meta embeddings with the collaborative information. CoMeta consists of two submodules: B-EG and S-EG. Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensive experiments on two public datasets. The experimental results demonstrate both the effectiveness and the compatibility of CoMeta.
7. A Human Subject Study of Named Entity Recognition (NER) in Conversational Music Recommendation Queries
Elena V. Epure, Romain Hennequin
https://arxiv.org/abs/2303.06944
We conducted a human subject study of named entity recognition on a noisy corpus of conversational music recommendation queries, with many irregular and novel named entities. We evaluated the human NER linguistic behaviour in these challenging conditions and compared it with the most common NER systems nowadays, fine-tuned transformers. Our goal was to learn about the task to guide the design of better evaluation methods and NER algorithms. The results showed that NER in our context was quite hard for both human and algorithms under a strict evaluation schema; humans had higher precision, while the model higher recall because of entity exposure especially during pre-training; and entity types had different error patterns (e.g. frequent typing errors for artists). The released corpus goes beyond predefined frames of interaction and can support future work in conversational music recommendation.
8. AutoDenoise: Automatic Data Instance Denoising for Recommendations
Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang
https://arxiv.org/abs/2303.06611
Historical user-item interaction datasets are essential in training modern recommender systems for predicting user preferences. However, the arbitrary user behaviors in most recommendation scenarios lead to a large volume of noisy data instances being recorded, which cannot fully represent their true interests. While a large number of denoising studies are emerging in the recommender system community, all of them suffer from highly dynamic data distributions. In this paper, we propose a Deep Reinforcement Learning (DRL) based framework, AutoDenoise, with an Instance Denoising Policy Network, for denoising data instances with an instance selection manner in deep recommender systems. To be specific, AutoDenoise serves as an agent in DRL to adaptively select noise-free and predictive data instances, which can then be utilized directly in training representative recommendation models. In addition, we design an alternate two-phase optimization strategy to train and validate the AutoDenoise properly. In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability. We conduct extensive experiments to validate the effectiveness of AutoDenoise combined with multiple representative recommender system models.
9. MobileRec: A Large-Scale Dataset for Mobile Apps Recommendation
M.H. Maqbool, Umar Farooq, Adib Mosharrof, A.B. Siddique, Hassan Foroosh
https://arxiv.org/abs/2303.06588
Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the number of mobile users and applications (aka apps) has increased exponentially over the past decade, research in mobile app recommender systems has been significantly constrained, primarily due to the lack of high-quality benchmark datasets, as opposed to recommendations for products, movies, and news. To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. We constructed MobileRec from users' activity on the Google play store. MobileRec contains 19.3 million user interactions (i.e., user reviews on apps) with over 10K unique apps across 48 categories. MobileRec records the sequential activity of a total of 0.7 million distinct users. Each of these users has interacted with no fewer than five distinct apps, which stands in contrast to previous datasets on mobile apps that recorded only a single interaction per user. Furthermore, MobileRec presents users' ratings as well as sentiments on installed apps, and each app contains rich metadata such as app name, category, description, and overall rating, among others. We demonstrate that MobileRec can serve as an excellent testbed for app recommendation through a comparative study of several state-of-the-art recommendation approaches. The quantitative results can act as a baseline for other researchers to compare their results against. The MobileRec dataset is available at: https://huggingface.co/datasets/recmeapp/mobilerec.
10. User Retention-oriented Recommendation with Decision Transformer
Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, Dawei yin
https://arxiv.org/abs/2303.06347
Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the offline methods, which aim to optimize the policy without online interactions, suffer from the notorious stability problem in value estimation or unbounded variance in counterfactual policy evaluation. To this end, we propose optimizing user retention with Decision Transformer~(DT), which avoids the offline difficulty by translating the RL as an autoregressive problem. However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation. In this work, we, therefore, contribute a series of strategies for tackling the exposed issues. We first articulate an efficient reward prompt by weighted aggregation of meta embeddings for informative reward embedding. Then, we endow a weighted contrastive learning method to solve the discrepancy between training and inference. Furthermore, we design two robust offline metrics to measure user retention. Finally, the significant improvement in the benchmark datasets demonstrates the superiority of the proposed method.
11. AutoMLP: Automated MLP for Sequential Recommendations, WWW2023
Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo
https://arxiv.org/abs/2303.06337
Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.
12. Semi-supervised Adversarial Learning for Complementary Item Recommendation, WWW2023
Koby Bibas, Oren Sar Shalom, Dietmar Jannach
https://arxiv.org/abs/2303.05812
Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like co-purchase statistics. In certain online marketplaces, however, e.g., on online auction sites, constantly new items are added to the catalog. In such cases, complementary item recommendations are often based on item side-information due to a lack of interaction data. In this work, we propose a novel approach that can leverage both item side-information and labeled complementary item pairs to generate effective complementary recommendations for cold items, i.e., for items for which no co-purchase statistics yet exist. Given that complementary items typically have to be of a different category than the seed item, we technically maintain a latent space for each item category. Simultaneously, we learn to project distributed item representations into these category spaces to determine suitable recommendations. The main learning process in our architecture utilizes labeled pairs of complementary items. In addition, we adopt ideas from Cycle Generative Adversarial Networks (CycleGAN) to leverage available item information even in case no labeled data exists for a given item and category. Experiments on three e-commerce datasets show that our method is highly effective.
13. Fairness-aware Differentially Private Collaborative Filtering
Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang, Xiaoting Zhao, Yiming Ying
https://arxiv.org/abs/2303.09527
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, trained by differentially private stochastic gradient descent (DP-SGD), results in a disparate impact on user groups with respect to different user engagement levels. This, in turn, causes the original unfair model to become even more biased against inactive users. To address the above issues, we propose textbf{DP-Fair}, a two-stage framework for collaborative filtering based algorithms. Specifically, it combines differential privacy mechanisms with fairness constraints to protect user privacy while ensuring fair recommendations. The experimental results, based on Amazon datasets, and user history logs collected from Etsy, one of the largest e-commerce platforms, demonstrate that our proposed method exhibits superior performance in terms of both overall accuracy and user group fairness on both shallow and deep recommendation models compared to vanilla DP-SGD.
14. Graph-less Collaborative Filtering, WWW2023
Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu
https://arxiv.org/abs/2303.08537
Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with their inherently recursive message propagation among neighboring nodes, existing GNN-based CF models may generate indistinguishable and inaccurate user (item) representations due to the over-smoothing and noise effect with low-pass Laplacian smoothing operators. In addition, the recursive information propagation with the stacked aggregators in the entire graph structures may result in poor scalability in practical applications. Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning. In SimRec, adaptive transferring knowledge is enabled between the teacher GNN model and a lightweight student network, to not only preserve the global collaborative signals, but also address the over-smoothing issue with representation recalibration. Empirical results on public datasets show that SimRec archives better efficiency while maintaining superior recommendation performance compared with various strong baselines. Our implementations are publicly available at: https://github.com/HKUDS/SimRec
15. Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems, CHI2023
Krisztian Balog, Filip Radlinski, Andrey Petrov
https://arxiv.org/abs/2303.09498
Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users' choices. This paper presents an experimental protocol for measuring the degree to which positively or negatively biased explanations can lead to users choosing suboptimal recommendations. Key elements of this protocol include a preference elicitation stage to allow for personalizing recommendations, manual identification and extraction of item aspects from reviews, and a controlled method for introducing bias through the combination of both positive and negative aspects. We study explanations in two different textual formats: as a list of item aspects and as fluent natural language text. Through a user study with 129 participants, we demonstrate that explanations can significantly affect users' selections and that these findings generalize across explanation formats.
16. P-MMF: Provider Max-min Fairness Re-ranking in Recommender System, WWW2023
Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, Zhenghua Dong
https://arxiv.org/abs/2303.06660
In this paper, we address the issue of recommending fairly from the aspect of providers, which has become increasingly essential in multistakeholder recommender systems. Existing studies on provider fairness usually focused on designing proportion fairness (PF) metrics that first consider systematic fairness. However, sociological researches show that to make the market more stable, max-min fairness (MMF) is a better metric. The main reason is that MMF aims to improve the utility of the worst ones preferentially, guiding the system to support the providers in weak market positions. When applying MMF to recommender systems, how to balance user preferences and provider fairness in an online recommendation scenario is still a challenging problem. In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem. Specifically, P-MMF formulates provider fair recommendation as a resource allocation problem, where the exposure slots are considered the resources to be allocated to providers and the max-min fairness is used as the regularizer during the process. We show that the problem can be further represented as a regularized online optimizing problem and solved efficiently in its dual space. During the online re-ranking phase, a momentum gradient descent method is designed to conduct the dynamic re-ranking. Theoretical analysis showed that the regret of P-MMF can be bounded. Experimental results on four public recommender datasets demonstrated that P-MMF can outperformed the state-of-the-art baselines. Experimental results also show that P-MMF can retain small computationally costs on a corpus with the large number of items.
17. Vertical Federated Graph Neural Network for Recommender System
Peihua Mai, Yan Pang
https://arxiv.org/abs/2303.05786
Conventional recommender systems are required to train the recommendation model using a centralized database. However, due to data privacy concerns, this is often impractical when multi-parties are involved in recommender system training. Federated learning appears as an excellent solution to the data isolation and privacy problem. Recently, Graph neural network (GNN) is becoming a promising approach for federated recommender systems. However, a key challenge is to conduct embedding propagation while preserving the privacy of the graph structure. Few studies have been conducted on the federated GNN-based recommender system. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN. We design a framework to transmit: (i) the summation of neighbor embeddings using random projection, and (ii) gradients of public parameter perturbed by ternary quantization mechanism. Empirical studies show that VerFedGNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users' interaction information.
18. Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples
Ali Montazeralghaem, James Allan
https://arxiv.org/abs/2303.05575
Conversational recommender systems (CRSs) are improving rapidly, according to the standard recommendation accuracy metrics. However, it is essential to make sure that these systems are robust in interacting with users including regular and malicious users who want to attack the system by feeding the system modified input data. In this paper, we propose an adversarial evaluation scheme including four scenarios in two categories and automatically generate adversarial examples to evaluate the robustness of these systems in the face of different input data. By executing these adversarial examples we can compare the ability of different conversational recommender systems to satisfy the user's preferences. We evaluate three CRSs by the proposed adversarial examples on two datasets. Our results show that none of these systems are robust and reliable to the adversarial examples.
