其他
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
本文精选了上周（0509-0515）最新发布的20篇推荐系统相关论文，方向主要包括会话推荐[1,6,12,13]、基于强化学习的推荐[7,16]、基于对比学习的推荐[5]、鲁棒推荐[9]、公平性推荐[10]、时尚推荐[18]等的推荐算法，应用涵盖会话推荐、序列推荐、音乐推荐、链接推荐、论文提交推荐以及新闻推荐等。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. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
2. Link recommendations: Their impact on network structure and minorities
3. TaDeR: A New Task Dependency Recommendation for Project Management Platform
4. FPSRS: A Fusion Approach for Paper Submission Recommendation System
5. SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System
6. Bayesian Prior Learning via Neural Networks for Next-item Recommendation
7. State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
8. Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
9. Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
10. Selective Fairness in Recommendation via Prompts
11. Risk Aversion In Learning Algorithms and an Application To Recommendation Systems
12. Effectively Using Long and Short Sessions for Multi-Session-based Recommendations
13. Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
14. Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
15. Psychologically-Inspired Music Recommendation System
16. Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
17. Visual Data Analysis with Task-based Recommendations
18. End-to-End Image-Based Fashion Recommendation
19. Tensor-based Collaborative Filtering With Smooth Ratings Scale
20. Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
1. Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
https://arxiv.org/abs/2205.06058News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit feedback from user behaviors, which indicates what users really like or dislike. Hence, we propose a comprehensive framework to model user behaviors through positive feedback (i.e., the articles they spend more time on) and negative feedback (i.e., the articles they choose to skip without clicking in). Moreover, the framework implicitly models the user using their session start time, and the article using its initial publishing time, in what we call "neutral feedback". Empirical evaluation on three real-world news datasets shows the framework's promising performance of more accurate, diverse and even unexpectedness recommendations than other state-of-the-art session-based recommendation approaches.Shansan Gong, Kenny Q. Zhu
2. Link recommendations: Their impact on network structure and minorities
https://arxiv.org/abs/2205.06048Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group. Our systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities.Antonio Ferrara, Lisette Espín-Noboa, Fariba Karimi, Claudia Wagner
3. TaDeR: A New Task Dependency Recommendation for Project Management Platform
https://arxiv.org/abs/2205.05976Many startups and companies worldwide have been using project management software and tools to monitor, track and manage their projects. For software projects, the number of tasks from the beginning to the end is quite a large number that sometimes takes a lot of time and effort to search and link the current task to a group of previous ones for further references. This paper proposes an efficient task dependency recommendation algorithm to suggest tasks dependent on a given task that the user has just created. We present an efficient feature engineering step and construct a deep neural network to this aim. We performed extensive experiments on two different large projects (MDLSITE and FLUME) to find the best features in 28 combinations of features and the best performance model using two embedding methods (GloVe and FastText). We consider three types of models (GRU, CNN, LSTM) using Accuracy@K, MRR@K, and Recall@K (where K = 1, 2, 3, and 5) and baseline models using traditional methods: TF-IDF with various matching score calculating such as cosine similarity, Euclidean distance, Manhattan distance, and Chebyshev distance. After many experiments, the GloVe Embedding and CNN model reached the best result in our dataset, so we chose this model as our proposed method. In addition, adding the time filter in the post-processing step can significantly improve the recommendation system's performance. The experimental results show that our proposed method can reach 0.2335 in Accuracy@1 and MRR@1 and 0.2011 in Recall@1 of dataset FLUME. With the MDLSITE dataset, we obtained 0.1258 in Accuracy@1 and MRR@1 and 0.1141 in Recall@1. In the top 5, our model reached 0.3040 in Accuracy@5, 0.2563 MRR@5, and 0.2651 Recall@5 in FLUME. In the MDLSITE dataset, our model got 0.5270 Accuracy@5, 0.2689 MRR@5, and 0.2651 Recall@5.Quynh Nguyen, Dac H. Nguyen, Son T. Huynh, Hoa K. Dam, Binh T. Nguyen
4. FPSRS: A Fusion Approach for Paper Submission Recommendation System
https://arxiv.org/abs/2205.05965Recommender systems have been increasingly popular in entertainment and consumption and are evident in academics, especially for applications that suggest submitting scientific articles to scientists. However, because of the various acceptance rates, impact factors, and rankings in different publishers, searching for a proper venue or journal to submit a scientific work usually takes a lot of time and effort. In this paper, we aim to present two newer approaches extended from our paper [13] presented at the conference IAE/AIE 2021 by employing RNN structures besides using Conv1D. In addition, we also introduce a new method, namely DistilBertAims, using DistillBert for two cases of uppercase and lower-case words to vectorize features such as Title, Abstract, and Keywords, and then use Conv1d to perform feature extraction. Furthermore, we propose a new calculation method for similarity score for Aim & Scope with other features; this helps keep the weights of similarity score calculation continuously updated and then continue to fit more data. The experimental results show that the second approach could obtain a better performance, which is 62.46% and 12.44% higher than the best of the previous study [13] in terms of the Top 1 accuracy.Son T. Huynh, Nhi Dang, Dac H. Nguyen, Phong T. Huynh, Binh T. Nguyen
5. SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System
https://arxiv.org/abs/2205.05940The recommendation system plays a vital role in many areas, especially academic fields, to support researchers in submitting and increasing the acceptance of their work through the conference or journal selection process. This study proposes a transformer-based model using transfer learning as an efficient approach for the paper submission recommendation system. By combining essential information (such as the title, the abstract, and the list of keywords) with the aims and scopes of journals, the model can recommend the Top K journals that maximize the acceptance of the paper. Our model had developed through two states: (i) Fine-tuning the pre-trained language model (LM) with a simple contrastive learning framework. We utilized a simple supervised contrastive objective to fine-tune all parameters, encouraging the LM to learn the document representation effectively. (ii) The fine-tuned LM was then trained on different combinations of the features for the downstream task. This study suggests a more advanced method for enhancing the efficiency of the paper submission recommendation system compared to previous approaches when we respectively achieve 0.5173, 0.8097, 0.8862, 0.9496 for Top 1, 3, 5, and 10 accuracies on the test set for combining the title, abstract, and keywords as input features. Incorporating the journals' aims and scopes, our model shows an exciting result by getting 0.5194, 0.8112, 0.8866, and 0.9496 respective to Top 1, 3, 5, and 10.Duc H. Le, Tram T. Doan, Son T. Huynh, Binh T. Nguyen
6. Bayesian Prior Learning via Neural Networks for Next-item Recommendation
https://arxiv.org/abs/2205.05209Next-item prediction is a a popular problem in the recommender systems domain. As the name suggests, the task is to recommend subsequent items that a user would be interested in given contextual information and historical interaction data. In our paper, we model a general notion of context via a sequence of item interactions. We model the next item prediction problem using the Bayesian framework and capture the probability of appearance of a sequence through the posterior mean of the Beta distribution. We train two neural networks to accurately predict the alpha & beta parameter values of the Beta distribution. Our novel approach of combining black-box style neural networks, known to be suitable for function approximation with Bayesian estimation methods have resulted in an innovative method that outperforms various state-of-the-art baselines. We demonstrate the effectiveness of our method in two real world datasets. Our framework is an important step towards the goal of building privacy preserving recommender systems.Manoj Reddy Dareddy, Zijun Xue, Nicholas Lin, Junghoo Cho
7. State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
https://arxiv.org/abs/2205.04797Methods for reinforcement learning for recommendation (RL4Rec) are increasingly receiving attention as they can quickly adapt to user feedback. A typical RL4Rec framework consists of (1) a state encoder to encode the state that stores the users' historical interactions, and (2) an RL method to take actions and observe rewards. Prior work compared four state encoders in an environment where user feedback is simulated based on real-world logged user data. An attention-based state encoder was found to be the optimal choice as it reached the highest performance. However, this finding is limited to the actor-critic method, four state encoders, and evaluation-simulators that do not debias logged user data. In response to these shortcomings, we reproduce and expand on the existing comparison of attention-based state encoders (1) in the publicly available debiased RL4Rec SOFA simulator with (2) a different RL method, (3) more state encoders, and (4) a different dataset. Importantly, our experimental results indicate that existing findings do not generalize to the debiased SOFA simulator generated from a different dataset and a Deep Q-Network (DQN)-based method when compared with more state encoders.Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke
8. Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards
https://arxiv.org/abs/2205.04702Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memory capacity requirements, reaching hundreds of GBs to TBs of model size. In RecSys, the so-called embedding layers account for the majority of memory usage so current systems employ a hybrid CPU-GPU design to have the large CPU memory store the memory hungry embedding layers. Unfortunately, training embeddings involve several memory bandwidth intensive operations which is at odds with the slow CPU memory, causing performance overheads. Prior work proposed to cache frequently accessed embeddings inside GPU memory as means to filter down the embedding layer traffic to CPU memory, but this paper observes several limitations with such cache design. In this work, we present a fundamentally different approach in designing embedding caches for RecSys. Our proposed ScratchPipe architecture utilizes unique properties of RecSys training to develop an embedding cache that not only sees the past but also the "future" cache accesses. ScratchPipe exploits such property to guarantee that the active working set of embedding layers can "always" be captured inside our proposed cache design, enabling embedding layer training to be conducted at GPU memory speed.Youngeun Kwon, Minsoo Rhu
9. Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
https://arxiv.org/abs/2205.04701In recommender systems, users always choose favorite items to rate, which results in data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) method and its variants have been widely studied and demonstrate superior performance. However, we show that DR methods are unstable to extremely small propensities and rely on extrapolations, resulting in sub-optimal performances. In this paper, we propose a stabilized doubly robust (SDR) estimator to address the above limitations while retaining double robustness. Theoretical analysis shows that SDR has bounded bias, variance and generalization error bound under inaccurate imputed errors and arbitrarily small propensities. In addition, we propose a novel learning approach for SDR that updates the imputation, propensity, and prediction models cyclically, achieving more stable and accurate predictions. Extensive experiments show that our approach significantly outperforms the existing methods.Haoxuan Li, Chunyuan Zheng, Xiao-Hua Zhou, Peng Wu
10. Selective Fairness in Recommendation via Prompts
https://arxiv.org/abs/2205.04682Recommendation fairness has attracted great attention recently. In real-world systems, users usually have multiple sensitive attributes (e.g. age, gender, and occupation), and users may not want their recommendation results influenced by those attributes. Moreover, which of and when these user attributes should be considered in fairness-aware modeling should depend on users' specific demands. In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free. We propose a novel parameter-efficient prompt-based fairness-aware recommendation (PFRec) framework, which relies on attribute-specific prompt-based bias eliminators with adversarial training, enabling selective fairness with different attribute combinations on sequential recommendation. Both task-specific and user-specific prompts are considered. We conduct extensive evaluations to verify PFRec's superiority in selective fairness.Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He
11. Risk Aversion In Learning Algorithms and an Application To Recommendation Systems
https://arxiv.org/abs/2205.04619Consider a bandit learning environment. We demonstrate that popular learning algorithms such as Upper Confidence Band (UCB) and -Greedy exhibit risk aversion: when presented with two arms of the same expectation, but different variance, the algorithms tend to not choose the riskier, i.e. higher variance, arm. We prove that -Greedy chooses the risky arm with probability tending to when faced with a deterministic and a Rademacher-distributed arm. We show experimentally that UCB also shows risk-averse behavior, and that risk aversion is present persistently in early rounds of learning even if the riskier arm has a slightly higher expectation. We calibrate our model to a recommendation system and show that algorithmic risk aversion can decrease consumer surplus and increase homogeneity. We discuss several extensions to other bandit algorithms, reinforcement learning, and investigate the impacts of algorithmic risk aversion for decision theory.Andreas Haupt, Aroon Narayanan
12. Effectively Using Long and Short Sessions for Multi-Session-based Recommendations
https://arxiv.org/abs/2205.04366It is not accurate to make recommendations only based one single current session. Therefore, multi-session-based recommendation(MSBR) is a solution for the problem. Compared with the previous MSBR models, we have made three improvements in this paper. First, the previous work choose to use all the history sessions of the user and/or of his similar users. When the user's current interest changes greatly from the past, most of these sessions can only have negative impacts. Therefore, we select a large number of randomly chosen sessions from the dataset as candidate sessions to avoid over depending on history data. Then we only choose to use the most similar sessions to get the most useful information while reduce the noise caused by dissimilar sessions. Second, in real-world datasets, short sessions account for a large proportion. The RNN often used in previous work is not suitable to process short sessions, because RNN only focuses on the sequential relationship, which we find is not the only relationship between items in short sessions. So, we designed a more suitable method named GAFE based on attention to process short sessions. Third, Although there are few long sessions, they can not be ignored. Not like previous models, which simply process long sessions in the same way as short sessions, we propose LSIS, which can split the interest of long sessions, to make better use of long sessions. Finally, to help recommendations, we also have considered users' long-term interests captured by a multi-layer GRU. Considering the four points above, we built the model ENIREC. Experiments on two real-world datasets show that the comprehensive performance of ENIREC is better than other existing models.Zihan Wang, Gang Wu, Yan Wang
13. Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
https://arxiv.org/abs/2205.04181Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices.Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin
14. Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
https://arxiv.org/abs/2205.03931Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Our work makes three key contributions. First, we find that models can identify patient self-reported race from clinical notes even when the notes are stripped of explicit indicators of race. Second, we determine that human experts are not able to accurately predict patient race from the same redacted clinical notes. Finally, we demonstrate the potential harm of this implicit information in a simulation study, and show that models trained on these race-redacted clinical notes can still perpetuate existing biases in clinical treatment decisions.Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi
15. Psychologically-Inspired Music Recommendation System
https://arxiv.org/abs/2205.03459In the last few years, automated recommendation systems have been a major focus in the music field, where companies such as Spotify, Amazon, and Apple are competing in the ability to generate the most personalized music suggestions for their users. One of the challenges developers still fail to tackle is taking into account the psychological and emotional aspects of the music. Our goal is to find a way to integrate users' personal traits and their current emotional state into a single music recommendation system with both collaborative and content-based filtering. We seek to relate the personality and the current emotional state of the listener to the audio features in order to build an emotion-aware MRS. We compare the results both quantitatively and qualitatively to the output of the traditional MRS based on the Spotify API data to understand if our advancements make a significant impact on the quality of music recommendations.Danila Rozhevskii, Jie Zhu, Boyuan Zhao
16. Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning
https://arxiv.org/abs/2205.03219Recommending a sequence of activities for an ongoing case requires that the recommendations conform to the underlying business process and meet the performance goal of either completion time or process outcome. Existing work on next activity prediction can predict the future activity but cannot provide guarantees of the prediction being conformant or meeting the goal. Hence, we propose a goal-oriented next best activity recommendation. Our proposed framework uses a deep learning model to predict the next best activity and an estimated value of a goal given the activity. A reinforcement learning method explores the sequence of activities based on the estimates likely to meet one or more goals. We further address a real-world problem of multiple goals by introducing an additional reward function to balance the outcome of a recommended activity and satisfy the goal. We demonstrate the effectiveness of the proposed method on four real-world datasets with different characteristics. The results show that the recommendations from our proposed approach outperform in goal satisfaction and conformance compared to the existing state-of-the-art next best activity recommendation techniques.Prerna Agarwal, Avani Gupta, Renuka Sindhgatta, Sampath Dechu
17. Visual Data Analysis with Task-based Recommendations
https://arxiv.org/abs/2205.03183General visualization recommendation systems typically make design decisions of the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce combination recommendation, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.Leixian Shen, Enya Shen, Zhiwei Tai, Yihao Xu, Jianmin Wang
18. End-to-End Image-Based Fashion Recommendation
https://arxiv.org/abs/2205.02923In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated deep neural networks, comparisons to attribute-aware models are often disregarded despite their ability to be easily extended to leverage items' image features. In this paper, we propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning in item recommendation tasks. The proposed model utilizes items' image features extracted by a calibrated ResNet50 component. We present an ablation study to compare incorporating the image features using three different techniques into the recommender system component that can seamlessly leverage any available items' attributes. Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models.Shereen Elsayed, Lukas Brinkmeyer, Lars Schmidt-Thieme
19. Tensor-based Collaborative Filtering With Smooth Ratings Scale
https://arxiv.org/abs/2205.05070Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.Nikita Marin, Elizaveta Makhneva, Maria Lysyuk, Vladimir Chernyy, Ivan Oseledets, Evgeny Frolov
20. Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
https://arxiv.org/abs/2205.04490Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the content information may be abundant, but also noisy and expensive to acquire. Thus, selection of particular features that improve cold-start recommendations becomes an important and non-trivial task. In the recent approach by Nembrini et al., the feature selection is driven by the correlational compatibility between collaborative and content-based models. The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave. Inspired by the reported results, we contend the idea that current quantum annealers are superior for this problem and instead focus on classical algorithms. In particular, we tackle QUBO via TTOpt, a recently proposed black-box optimizer based on tensor networks and multilinear algebra. We show the computational feasibility of this method for large problems with thousands of features, and empirically demonstrate that the solutions found are comparable to the ones obtained with D-Wave across all examined datasets.Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan Oseledets, Evgeny Frolov