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POI推荐算法[1]、基于对比变分自编码器的序列推荐算法[2]、书籍推荐算法[3]、序列推荐数据集[4]、对话推荐[6]、单类推荐算法[7]、序列推荐中的多样性建模[9]、实时推荐算法[10]、基于多层级对比学习的序列推荐算法[11]、一种用于推荐系统的简化图卷积范式[12]、推荐系统场景中时间感知的自注意力机制邂逅逻辑推理[13]、推荐算法中的因果推理综述[14]--最新综述 | 基于因果推断的推荐系统等。
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. A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals 2. ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation, CIKM2022 3. Hidden Author Bias in Book Recommendation, RecSys2022 workshop 4. MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation, RecSys2022 workshop 5. Robots as Mental Well-being Coaches: Design and Ethical Recommendations 6. Rethinking Conversational Recommendations: Is Decision Tree All You Need? 7. One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations 8. CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment, RecSys2022 9. Understanding Diversity in Session-Based Recommendation 10. SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning 11. Multi-level Contrastive Learning Framework for Sequential Recommendation 12. SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation, CIKM2022 13. Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems 14. Causal Inference in Recommender Systems: A Survey and Future Directions 15. Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen 16. Modelling the Recommender Alignment Problem
1. A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Zhu Sun, Yu Lei, Lu Zhang, Chen Li, Yew-Soon Ong, Jie Zhang
https://arxiv.org/abs/2209.00472
Current study on next POI recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. Being equipped with three modules (i.e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.
2. ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation, CIKM2022
Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu
https://arxiv.org/abs/2209.00456
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of Variational AutoEncoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at:
https://github.com/YuWang-1024/ContrastVAE
3. Hidden Author Bias in Book Recommendation, RecSys2022 workshop
Savvina Daniil, Mirjam Cuper, Cynthia C.S. Liem, Jacco van Ossenbruggen, Laura Hollink
https://arxiv.org/abs/2209.00371
Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness related issues, like popularity bias. In this work, we argue that popularity bias often leads to other biases that are not obvious when additional user or item information is not provided to the researcher. We examine our hypothesis in the book recommendation case on a commonly used dataset with book ratings. We enrich it with author information using publicly available external sources. We find that popular books are mainly written by US citizens in the dataset, and that these books tend to be recommended disproportionally by popular collaborative filtering algorithms compared to the users' profiles. We conclude that the societal implications of popularity bias should be further examined by the scholar community.
4. MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation, RecSys2022 workshop
Aleksandr Petrov, Ildar Safilo, Daria Tikhonovich, Dmitry Ignatov
https://arxiv.org/abs/2209.00325
We present a new movie and TV show recommendation dataset collected from the real users of MTS Kion video-on-demand platform. In contrast to other popular movie recommendation datasets, such as MovieLens or Netflix, our dataset is based on the implicit interactions registered at the watching time, rather than on explicit ratings. We also provide rich contextual and side information including interactions characteristics (such as temporal information, watch duration and watch percentage), user demographics and rich movies meta-information. In addition, we describe the MTS Kion Challenge - an online recommender systems challenge that was based on this dataset - and provide an overview of the best performing solutions of the winners. We keep the competition sandbox open, so the researchers are welcome to try their own recommendation algorithms and measure the quality on the private part of the dataset.
5. Robots as Mental Well-being Coaches: Design and Ethical Recommendations
Minja Axelsson, Micol Spitale, Hatice Gunes
https://arxiv.org/abs/2208.14874
The last decade has shown a growing interest in robots as well-being coaches. However, cohesive and comprehensive guidelines for the design of robots as coaches to promote mental well-being have not yet been proposed. This paper details design and ethical recommendations based on a qualitative meta-analysis drawing on a grounded theory approach, which was conducted with three distinct user-centered design studies involving robotic well-being coaches, namely: (1) a participatory design study conducted with 11 participants consisting of both prospective users who had participated in a Brief Solution-Focused Practice study with a human coach, as well as coaches of different disciplines, (2) semi-structured individual interview data gathered from 20 participants attending a Positive Psychology intervention study with the robotic well-being coach Pepper, and (3) a participatory design study conducted with 3 participants of the Positive Psychology study as well as 2 relevant well-being coaches. After conducting a thematic analysis and a qualitative meta-analysis, we collated the data gathered into convergent and divergent themes, and we distilled from those results a set of design guidelines and ethical considerations. Our findings can inform researchers and roboticists on the key aspects to take into account when designing robotic mental well-being coaches.
6. Rethinking Conversational Recommendations: Is Decision Tree All You Need?
A S M Ahsan-Ul Haque, Hongning Wang
https://arxiv.org/abs/2208.14614
Conversational recommender systems (CRS) dynamically obtain the user preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticised for lacking interpretability and requiring a large amount of training data to perform.
In this paper, we explore a simpler alternative and propose a decision tree based solution to CRS. The underlying challenge in CRS is that the same item can be described differently by different users. We show that decision trees are sufficient to characterize the interactions between users and items, and solve the key challenges in multi-turn CRS: namely which questions to ask, how to rank the candidate items, when to recommend, and how to handle negative feedback on the recommendations. Firstly, the training of decision trees enables us to find questions which effectively narrow down the search space. Secondly, by learning embeddings for each item and tree nodes, the candidate items can be ranked based on their similarity to the conversation context encoded by the tree nodes. Thirdly, the diversity of items associated with each tree node allows us to develop an early stopping strategy to decide when to make recommendations. Fourthly, when the user rejects a recommendation, we adaptively choose the next decision tree to improve subsequent questions and recommendations. Extensive experiments on three publicly available benchmark CRS datasets show that our approach provides significant improvement to the state of the art CRS methods.
7. One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations
Ramin Raziperchikolaei, Young-joo Chung
https://arxiv.org/abs/2208.14594
In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related user-item pairs among a large number of pairs with unknown interactions. Most previous loss functions rely on dissimilar pairs of users and items, which are selected from the ones with unknown interactions, to obtain better prediction performance. This strategy introduces several challenges such as increasing training time and hurting the performance by picking "similar pairs with the unknown interactions" as dissimilar pairs. In this paper, the goal is to only use the similar set to train the models. We point out three trivial solutions that the models converge to when they are trained only on similar pairs: collapsed, partially collapsed, and shrinking solutions. We propose two terms that can be added to the objective functions in the literature to avoid these solutions. The first one is a hinge pairwise distance loss that avoids the shrinking and collapsed solutions by keeping the average pairwise distance of all the representations greater than a margin. The second one is an orthogonality term that minimizes the correlation between the dimensions of the representations and avoids the partially collapsed solution. We conduct experiments on a variety of tasks on public and real-world datasets. The results show that our approach using only similar pairs outperforms state-of-the-art methods using similar pairs and a large number of dissimilar pairs.
8. CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment, RecSys2022
Rui Ma, Ning Liu, Jingsong Yuan, Huafeng Yang, Jiandong Zhang
https://arxiv.org/abs/2208.13480
Traditional recommendation systems mainly focus on modeling user interests. However, the dynamics of recommended items caused by attribute modifications (e.g. changes in prices) are also of great importance in real systems, especially in the fast-growing e-commerce environment, which may cause the users' demands to emerge, shift and disappear. Recent studies that make efforts on dynamic item representations treat the item attributes as side information but ignore its temporal dependency, or model the item evolution with a sequence of related users but do not consider item attributes. In this paper, we propose Core Attribute Evolution Network (CAEN), which partitions the user sequence according to the attribute value and thus models the item evolution over attribute dynamics with these users. Under this framework, we further devise a hierarchical attention mechanism that applies attribute-aware attention for user aggregation under each attribute, as well as personalized attention for activating similar users in assessing the matching degree between target user and item. Results from the extensive experiments over actual e-commerce datasets show that our approach outperforms the state-of-art methods and achieves significant improvements on the items with rapid changes over attributes, therefore helping the item recommendation to adapt to the growth of the e-commerce platform.
9. Understanding Diversity in Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun, Yew-Soon Ong
https://arxiv.org/abs/2208.13453
Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of diversity. Besides, the asserted "trade-off" relationship between accuracy and diversity has been increasingly questioned in the literature. Towards the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and diversity, striving for better understanding the diversity-related issues for SBRSs and providing guidance on designing diversified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and diversified SBRSs, by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. Our empirical results unveil that: 1) non-diversified methods can also obtain satisfying performance on diversity, which might even surpass diversified ones; and 2) the relationship between accuracy and diversity is quite complex. Besides the "trade-off" relationship, they might be positively correlated with each other, that is, having a same-trend (win-win or lose-lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on diversity in SBRSs (i.e., granularity of item categorization, session diversity of datasets, and length of recommendation lists).
10. SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
Baihan Lin
https://arxiv.org/abs/2208.13077
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time. Our system uses a turn-level rating mechanism that predicts the therapeutic outcome by computing a similarity score between the deep embedding of a scoring inventory, and the current sentence that the patient is speaking. The system automatically transcribes a continuous audio stream and separates it into turns of the patient and of the therapist using an online registration-free diarization method. The dialogue pairs along with their computed ratings are then fed into a deep reinforcement learning recommender where the sessions are treated as users and the topics are treated as items. Other than evaluating the empirical advantages of the core components on existing datasets, we demonstrate the effectiveness of this system in a web app.
11. Multi-level Contrastive Learning Framework for Sequential Recommendation
Ziyang Wang, Huoyu Liu, Wei Wei, Yue Hu, Xian-Ling Mao, Shaojian He, Rui Fang, Dangyang chen
https://arxiv.org/abs/2208.13007
Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
12. SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation, CIKM2022
Shaowen Peng, Sugiyama Kazunari, Tsunenori Mine
https://arxiv.org/abs/2208.12689
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow training convergence; (2) the notorious over-smoothing issue which reduces performance as stacking graph convolution layers. We argue that the above limitations are due to the lack of a deep understanding of GCN-based methods. To this end, we first investigate what design makes GCN effective for recommendation. By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits -largest singular vectors for recommendation. To alleviate the over-smoothing issue, we propose a renormalization trick to adjust the singular value gap, resulting in significant improvement. Extensive experiments on three real-world datasets show that our proposed SVD-GCN not only significantly outperforms state-of-the-arts but also achieves over 100x and 10x speedups over LightGCN and MF, respectively.
13. Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems
Zhijian Luo, Zihan Huang, Jiahui Tang, Yueen Hou, Yanzeng Gao
https://arxiv.org/abs/2208.13330
At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommendation model incorporated with temporal context would tend to be self-attentive, i.e., automatically focus more (less) on the relevance (irrelevance), respectively.
14. Causal Inference in Recommender Systems: A Survey and Future Directions
Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
https://arxiv.org/abs/2208.12397
Existing recommender systems extract the user preference based on learning the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, regretfully, the real world is driven by causality rather than correlation, and correlation does not imply causation. For example, the recommender systems can recommend a battery charger to a user after buying a phone, in which the latter can serve as the cause of the former, and such a causal relation cannot be reversed. Recently, to address it, researchers in recommender systems have begun to utilize causal inference to extract causality, enhancing the recommender system. In this survey, we comprehensively review the literature on causal inference-based recommendation. At first, we present the fundamental concepts of both recommendation and causal inference as the basis of later content. We raise the typical issues that the non-causality recommendation is faced. Afterward, we comprehensively review the existing work of causal inference-based recommendation, based on a taxonomy of what kind of problem causal inference addresses. Last, we discuss the open problems in this important research area, along with interesting future works.
15. Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
Eduard Zubchuk, Mikhail Arhipkin, Dmitry Menshikov, Aleksandr Karaush, Nikolay Mikhaylovskiy
https://arxiv.org/abs/2208.12356
We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trained separately, we show that using the fastText model as a vectorizer delivers competitive results.
16. Modelling the Recommender Alignment Problem
Francisco Carvalho
https://arxiv.org/abs/2208.12299
Recommender systems (RS) mediate human experience online. Most RS act to optimize metrics that are imperfectly aligned with the best-interest of users but are easy to measure, like ad-clicks and user engagement. This has resulted in a host of hard-to-measure side-effects: political polarization, addiction, fake news. RS design faces a recommender alignment problem: that of aligning recommendations with the goals of users, system designers, and society as a whole. But how do we test and compare potential solutions to align RS? Their massive scale makes them costly and risky to test in deployment. We synthesized a simple abstract modelling framework to guide future work.