论文周报 | 推荐系统领域最新研究进展
嘿，记得给“机器学习与推荐算法”添加星标
本次论文集合的方向主要包括跨域推荐算法[1]、基于自监督学习的推荐算法[2,6]、基于联邦学习的推荐算法[3,10]、基于置信度校准的推荐算法[4]、基于知识抽取的在线推荐训练框架[5]、实时端侧的短视频推荐算法[8]、推荐系统中的偏差与去偏技术[9]、基于隐式会话上下文的推荐算法[11]、动态因果协同过滤[12]、基于用户侧的公平性推荐算法[16]、对话推荐系统[14,17]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. Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation, KDD2022 2. Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation, CIKM2022 3. Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation 4. Towards Confidence-aware Calibrated Recommendation, CIKM2022 5. KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging, CIKM2022 6. Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning, CIKM2022 7. HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations 8. Real-time Short Video Recommendation on Mobile Devices, CIKM2022 9. Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services 10. Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation, CIKM2022 11. Implicit Session Contexts for Next-Item Recommendations, CIKM2022 12. Dynamic Causal Collaborative Filtering, CIKM2022 13. Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective 14. Evaluating Conversational Recommender Systems 15. Matching Theory-based Recommender Systems in Online Dating, RecSys2022 16. Towards Principled User-side Recommender Systems, CIKM2022 17. Comparison-based Conversational Recommender System with Relative Bandit Feedback, SIGIR2021
1. Dynamic collaborative filtering Thompson Sampling for cross-domain advertisements recommendation, KDD2022
Shion Ishikawa, Young-joo Chung, Yu Hirate
https://arxiv.org/abs/2208.11926
Recently online advertisers utilize Recommender systems (RSs) for display advertising to improve users' engagement. The contextual bandit model is a widely used RS to exploit and explore users' engagement and maximize the long-term rewards such as clicks or conversions. However, the current models aim to optimize a set of ads only in a specific domain and do not share information with other models in multiple domains. In this paper, we propose dynamic collaborative filtering Thompson Sampling (DCTS), the novel yet simple model to transfer knowledge among multiple bandit models. DCTS exploits similarities between users and between ads to estimate a prior distribution of Thompson sampling. Such similarities are obtained based on contextual features of users and ads. Similarities enable models in a domain that didn't have much data to converge more quickly by transferring knowledge. Moreover, DCTS incorporates temporal dynamics of users to track the user's recent change of preference. We first show transferring knowledge and incorporating temporal dynamics improve the performance of the baseline models on a synthetic dataset. Then we conduct an empirical analysis on a real-world dataset and the result showed that DCTS improves click-through rate by 9.7% than the state-of-the-art models. We also analyze hyper-parameters that adjust temporal dynamics and similarities and show the best parameter which maximizes CTR.
2. Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation, CIKM2022
Yuanliang Zhang, Xiaofeng Wang, Jinxin Hu, Ke Gao, Chenyi Lei, Fei Fang
https://arxiv.org/abs/2208.11457
Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enjoy portions of overlaps in users and items, while the distribution of different scenarios is different. The key point of multi-scenario modeling is to efficiently maximize the use of whole-scenario information and granularly generate adaptive representations both for users and items among multiple scenarios. we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios. (2) Insufficient exploitation of entire space samples. (3) Item's multi-scenario representation disentanglement problem. In this paper, we propose a Scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. Specifically, we design a Multi-Layer Scenario Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select and fuse effective transfer information from whole scenario to individual scenario in a quite fine-grained and decoupled way. To sufficiently exploit the power of entire space samples, a two-stage training process including pre-training and fine-tune is introduced. The pre-training stage is based on a scenario-supervised contrastive learning task with the training samples drawn from labeled and unlabeled data spaces. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. Extensive experimental results on public and industrial datasets demonstrate the superiority of the SASS model over state-of-the-art methods. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests.
3. Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Sichun Luo, Yuanzhang Xiao, Yang Liu, Congduan Li, Linqi Song
https://arxiv.org/abs/2208.10692
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, several vital challenges remain to be addressed: (i) The majority of federated recommendation models only consider the model performance and the privacy-preserving ability, while ignoring the optimization of the communication process; (ii) Most of the federated recommenders are designed for heterogeneous systems, causing unfairness problems during the federation process; (iii) The personalization techniques have been less explored in many federated recommender systems.
4. Towards Confidence-aware Calibrated Recommendation, CIKM2022
Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi, Nasim Sonboli
https://arxiv.org/abs/2208.10192
Recommender systems utilize users' historical data to learn and predict their future interests, providing them with suggestions tailored to their tastes. Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data. Mitigating miscalibration brings various benefits to a recommender system. For example, it becomes less likely that a system overlooks categories with less interaction on a user's profile by only recommending popular categories. Despite the notable success, calibration methods have several drawbacks, such as limiting the diversity of the recommended items and not considering the calibration confidence. This work, presents a set of properties that address various aspects of a desired calibrated recommender system. Considering these properties, we propose a confidence-aware optimization-based re-ranking algorithm to find the balance between calibration, relevance, and item diversity, while simultaneously accounting for calibration confidence based on user profile size. Our model outperforms state-of-the-art methods in terms of various accuracy and beyond-accuracy metrics for different user groups.
5. KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging, CIKM2022
Yujing Zhang, Zhangming Chan, Shuhao Xu, Weijie Bian, Shuguang Han, Hongbo Deng, Bo Zheng
https://arxiv.org/abs/2208.10174
An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback data to avoid the disturbance among different subsystems. However, we argue that such data usage may lead to sub-optimal online performance because of the data sparsity. To alleviate this issue, we propose to extract knowledge from the super-domain that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task). To this end, we propose a novel industrial KnowlEdge Extraction and Plugging (KEEP) framework, which is a two-stage framework that consists of 1) a supervised pre-training knowledge extraction module on super-domain, and 2) a plug-in network that incorporates the extracted knowledge into the downstream model. This makes it friendly for incremental training of online recommendation. Moreover, we design an efficient empirical approach for KEEP and introduce our hands-on experience during the implementation of KEEP in a large-scale industrial system. Experiments conducted on two real-world datasets demonstrate that KEEP can achieve promising results. It is notable that KEEP has also been deployed on the display advertising system in Alibaba, bringing a lift of CTR and RPM.
6. Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning, CIKM2022
Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, Dangyang Chen
https://arxiv.org/abs/2208.10061
Incorporating Knowledge Graphs (KG) into recommeder system has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, as: 1) the sparse interaction, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts.
7. HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
Sichun Luo, Xinyi Zhang, Yuanzhang Xiao, Linqi Song
https://arxiv.org/abs/2208.09586
The recent popularity of edge devices and Artificial Intelligent of Things (AIoT) has driven a new wave of contextual recommendations, such as location based Point of Interest (PoI) recommendations and computing resource-aware mobile app recommendations. In many such recommendation scenarios, contexts are drifting over time. For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time. However, most existing graph-based collaborative filtering methods are designed under the assumption of static features. Therefore, they would require frequent retraining and/or yield graphical models burgeoning in sizes, impeding their suitability for context-drifting recommendations.
8. Real-time Short Video Recommendation on Mobile Devices, CIKM2022
Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang
https://arxiv.org/abs/2208.09577
Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features. With more accurate predictions, we further consider interactions among candidate videos, and propose a context-aware re-ranking method based on adaptive beam search. The framework has been deployed on Kuaishou, a billion-user scale short video application, and improved effective view, like and follow by 1.28%, 8.22% and 13.6% respectively.
9. Exploring Popularity Bias in Music Recommendation Models and Commercial Steaming Services
Douglas R. Turnbull, Sean McQuillan, Vera Crabtree, John Hunter, Sunny Zhang
https://arxiv.org/abs/2208.09517
Popularity bias is the idea that a recommender system will unduly favor popular artists when recommending artists to users. As such, they may contribute to a winner-take-all marketplace in which a small number of artists receive nearly all of the attention, while similarly meritorious artists are unlikely to be discovered. In this paper, we attempt to measure popularity bias in three state-of-art recommender system models (e.g., SLIM, Multi-VAE, WRMF) and on three commercial music streaming services (Spotify, Amazon Music, YouTube). We find that the most accurate model (SLIM) also has the most popularity bias while less accurate models have less popularity bias. We also find no evidence of popularity bias in the commercial recommendations based on a simulated user experiment.
10. Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation, CIKM2022
Sichun Luo, Yuanzhang Xiao, Linqi Song
https://arxiv.org/abs/2208.09375
Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.
11. Implicit Session Contexts for Next-Item Recommendations, CIKM2022
Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
https://arxiv.org/abs/2208.09076
Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose ISCON, which implicitly contextualizes sessions. ISCON first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. ISCON then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that ISCON has superior next-item prediction accuracy than state-of-the-art models. A case study of ISCON on the Reddit dataset confirms that assigned session contexts are unique and meaningful.
12. Dynamic Causal Collaborative Filtering, CIKM2022
Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, Yongfeng Zhang
https://arxiv.org/abs/2208.11094
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content exposure, which if left unattended, may results in echo chambers. As a result, it is important to understand when the recommendations will lead to echo chambers and how to mitigate echo chambers without hurting the recommendation performance.
13. Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective
Yongwei Wang, Yong Liu, Zhiqi Shen
https://arxiv.org/abs/2208.09979
Graph neural networks (GNN) based collaborative filtering (CF) have attracted increasing attention in e-commerce and social media platforms. However, there still lack efforts to evaluate the robustness of such CF systems in deployment. Fundamentally different from existing attacks, this work revisits the item promotion task and reformulates it from a targeted topological attack perspective for the first time. Specifically, we first develop a targeted attack formulation to maximally increase a target item's popularity. We then leverage gradient-based optimizations to find a solution. However, we observe the gradient estimates often appear noisy due to the discrete nature of a graph, which leads to a degradation of attack ability. To resolve noisy gradient effects, we then propose a masked attack objective that can remarkably enhance the topological attack ability. Furthermore, we design a computationally efficient approach to the proposed attack, thus making it feasible to evaluate large-large CF systems. Experiments on two real-world datasets show the effectiveness of our attack in analyzing the robustness of GNN-based CF more practically.
14. Evaluating Conversational Recommender Systems
Dietmar Jannach
https://arxiv.org/abs/2208.12061
Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices.
15. Matching Theory-based Recommender Systems in Online Dating, RecSys2022
Yoji Tomita, Riku Togashi, Daisuke Moriwaki
https://arxiv.org/abs/2208.11384
Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender systems (RRSs) and matching theory. We present our ongoing project to deploy a matching theory-based recommender system (MTRS) in a real-world online dating platform.
16. Towards Principled User-side Recommender Systems, CIKM2022
Ryoma Sato
https://arxiv.org/abs/2208.09864
Traditionally, recommendation algorithms have been designed for service developers. However, recently, a new paradigm called user-side recommender systems has been proposed and they enable web service users to construct their own recommender systems without access to trade-secret data. This approach opens the door to user-defined fair systems even if the official recommender system of the service is not fair. While existing methods for user-side recommender systems have addressed the challenging problem of building recommender systems without using log data, they rely on heuristic approaches, and it is still unclear whether constructing user-side recommender systems is a well-defined problem from theoretical point of view. In this paper, we provide theoretical justification of user-side recommender systems. Specifically, we see that hidden item features can be recovered from the information available to the user, making the construction of user-side recommender system well-defined. However, this theoretically grounded approach is not efficient. To realize practical yet theoretically sound recommender systems, we propose three desirable properties of user-side recommender systems and propose an effective and efficient user-side recommender system, Consul, based on these foundations. We prove that Consul satisfies all three properties, whereas existing user-side recommender systems lack at least one of them. In the experiments, we empirically validate the theory of feature recovery via numerical experiments. We also show that our proposed method achieves an excellent trade-off between effectiveness and efficiency and demonstrate via case studies that the proposed method can retrieve information that the provider's official recommender system cannot.
17. Comparison-based Conversational Recommender System with Relative Bandit Feedback, SIGIR2021
Zhihui Xie, Tong Yu, Canzhe Zhao, Shuai Li
https://arxiv.org/abs/2208.09837
With the recent advances of conversational recommendations, the recommender system is able to actively and dynamically elicit user preference via conversational interactions. To achieve this, the system periodically queries users' preference on attributes and collects their feedback. However, most existing conversational recommender systems only enable the user to provide absolute feedback to the attributes. In practice, the absolute feedback is usually limited, as the users tend to provide biased feedback when expressing the preference. Instead, the user is often more inclined to express comparative preferences, since user preferences are inherently relative. To enable users to provide comparative preferences during conversational interactions, we propose a novel comparison-based conversational recommender system. The relative feedback, though more practical, is not easy to be incorporated since its feedback scale is always mismatched with users' absolute preferences. With effectively collecting and understanding the relative feedback from an interactive manner, we further propose a new bandit algorithm, which we call RelativeConUCB. The experiments on both synthetic and real-world datasets validate the advantage of our proposed method, compared to the existing bandit algorithms in the conversational recommender systems.