Reviewerly/exHarmony huggingface dataset:
Acknowledging Dynamic Aspects of Trust in Recommender Systems

Trust-based recommender systems emerged as a solution to different limitations of traditional recommender systems. These social systems rely on the assumption that users will adopt the preferences of users they deem trustworthy in an online social setting. However, most trust-based recommender systems consider trust to be a static notion, thereby disregarding crucial dynamic factors that influence the value of trust between users and the performance of the recommender system. In this work, we intend to address several challenges regarding the dynamics of trust within a social recommender system. These issues include the temporal evolution of trust between users and change detection and prediction in users’ interactions. By exploring the factors that influence the evolution of human trust, a complex and abstract concept, this work will contribute to a better understanding of how trust operates in recommender systems.
SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation

Session-based recommendation aims to predict the next item based on a set of anonymous sessions. Capturing user intent from a short interaction sequence imposes a variety of challenges since no user profiles are available and interaction data is naturally sparse. Recent approaches relying on graph neural networks (GNNs) for session-based recommendation use global item relations to explore collaborative information from different sessions. These methods capture the topological structure of the graph and rely on multi-hop information aggregation in GNNs to exchange information along edges. Consequently, graph-based models suffer from noisy item relations in the training data and introduce high complexity for large item catalogs. We propose to explicitly model the multi-hop information aggregation mechanism over multiple layers via shortest-path edges based on knowledge from the sequential recommendation domain. Our approach does not require multiple layers to exchange information and ignores unreliable item-item relations. Furthermore, to address inherent data sparsity, we are the first to apply supervised contrastive learning by mining data-driven positive and hard negative item samples from the training data. Extensive experiments on three different datasets show that the proposed approach outperforms almost all of the state-of-the-art methods.
From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware

In the last decade, large-scale deep learning has fundamentally transformed industrial recommendation systems. However, this revolutionary technology remains prohibitively expensive due to the need for costly and scarce specialized hardware, such as Graphics Processing Units (GPUs), to train and serve models. In this talk, we share our multi-year journey at ThirdAI in developing efficient neural recommendation models that can be trained and deployed on commodity CPU machines without the need for costly accelerators like GPUs. In particular, we discuss the limitations of the current GPU-based ecosystem in machine learning, why recommendation systems are amenable to the strengths of CPU devices, and present results from our efforts to translate years of academic research into a deployable system that fundamentally shifts the economics of training and operating large-scale machine learning models.
M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework

Users in recommender systems exhibit multi-behavior in multiple business scenarios on real-world e-commerce platforms. A crucial challenge in such systems is to make recommendations for each business scenario at the same time. On top of this, multiple predictions (e.g., Click Through Rate and Conversion Rate) need to be made simultaneously in order to improve the platform revenue. Research focus on making recommendations for several business scenarios is in the field of Multi-Scenario Recommendation (MSR), and Multi-Task Recommendation (MTR) mainly attempts to solve the possible problems in collaboratively executing different recommendation tasks. However, existing researchers have paid attention to either MSR or MTR, ignoring the integration of MSR and MTR that faces the issue of conflict between scenarios and tasks. To address the above issue, we propose a Meta-based Multi-scenario Multi-task RECommendation framework (M3REC) to serve multiple tasks in multiple business scenarios by a unified model. However, integrating MSR and MTR in a proper manner is non-trivial due to: 1) Unified representation problem: Users’ and items’ representation behave Non-i.i.d in different scenarios and tasks which takes inconsistency into recommendations. 2) Synchronous optimization problem: Tasks distribution varies in different scenarios, and a unified optimization method is needed to optimize multi-tasks in multi-scenarios. Thus, to unified represent users and items, we design a Meta-Item-Embedding Generator (MIEG) and a User-Preference Transformer (UPT). The MIEG module can generate initialized item embedding using item features through meta-learning technology, and the UPT module can transfer user preferences in other scenarios. Besides, the M3REC framework uses a specifically designed backbone network together with a task-specific aggregate gate to promote all tasks to achieve the purpose of optimizing multiple tasks in multiple business scenarios within one model. Experiments on two public datasets have shown that M3REC outperforms those compared MSR and MTR state-of-the-art methods.
Task Aware Feature Extraction Framework for Sequential Dependence Multi-Task Learning

In online recommendation, financial service, etc., the most common application of multi-task learning (MTL) is the multi-step conversion estimations. A core property of the multi-step conversion is the sequential dependence among tasks. However, most existing works focus far more on the specific post-view click-through rate (CTR) and post-click conversion rate (CVR) estimations, which neglect the generalization of sequential dependence multi-task learning (SDMTL). Additionally, the performance of the SDMTL framework is also deteriorated by the interference derived from implicitly conflict information passing between adjacent tasks. In this paper, a systematic learning paradigm of the SDMTL problem is established for the first time, which can transform the SDMTL problem into a general MTL problem with constraints and be applicable to more general multi-step conversion scenarios with stronger task dependence. Also, the distribution dependence relationship between adjacent task spaces is illustrated from a theoretical point of view. On the other hand, an SDMTL architecture, named Task Aware Feature Extraction (TAFE), is developed to enable dynamic task representation learning from a sample-wise view. TAFE selectively reconstructs the implicit shared information corresponding to each sample case and performs explicit task-specific extraction under dependence constraints. Extensive experiments on offline public and real-world industrial datasets, and online A/B implementations demonstrate the effectiveness and applicability of proposed theoretical and implementation frameworks.
Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation

End-to-end retrieval models, such as Tree-based Models (TDM) and Deep Retrieval (DR), have attracted a lot of attention, but they cannot handle cold-start and long-tail item recommendation scenarios well. Specifically, DR learns a compact indexing structure, enabling efficient and accurate retrieval for large recommendation systems. However, it is discovered that DR largely fails on retrieving cold-start and long-tail items. This is because DR only utilizes user-item interaction data, which is rare and often noisy for cold-start and long-tail items. Besides, end-to-end retrieval models are unable to make use of the rich item content features. To address this issue while maintaining the efficiency of DR indexing structure, we propose Full Index Deep Retrieval (FIDR) that learns indices for the full corpus items, including cold-start and long-tail items. In addition to the original structure in DR (called User Structure in FIDR) that learns with user-item interaction data (e.g., clicks), we add an Item Structure to embed items directly based on item content features (e.g., categories). With joint efforts of User Structure and Item Structure, FIDR makes cold-start items retrievable and also improves the recommendation quality of long-tail items. To our best knowledge, FIDR is the first to solve the cold-start and long-tail recommendation problem for the end-to-end retrieval models. Through extensive experiments on three real-world datasets, we demonstrate that FIDR can effectively recommend cold-start as well as long-tail items, and largely promote overall recommendation performance without sacrificing inference efficiency. According to the experiments, the recall of FIDR is improved by 8.8%~11.9%, while the inference of FIDR is as efficient as DR.
FAccTRec 2023: The 6th Workshop on Responsible Recommendation

The 6th Workshop on Responsible Recommendation (FAccTRec 2023) was held in conjunction with the 17th ACM Conference on Recommender Systems on September, 2023 at Singapore, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.
Enhanced Privacy Preservation for Recommender Systems

extended-abstract Share on Enhanced Privacy Preservation for Recommender Systems Author: Ziqing Wu School of Computer Science and Engineering, NTU, Singapore School of Computer Science and Engineering, NTU, Singapore 0000-0002-3714-0942View Profile Authors Info & Claims RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023Pages 1364–1368https://doi.org/10.1145/3604915.3608888Published:14 September 2023Publication History 0citation91DownloadsMetricsTotal Citations0Total Downloads91Last 12 Months91Last 6 weeks91 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Advancing Automation of Design Decisions in Recommender System Pipelines

Recommender systems have become essential in domains like streaming services, social media platforms, and e-commerce websites. However, the development of a recommender system involves a complex pipeline with preprocessing, data splitting, algorithm and model selection, and postprocessing stages. Every stage of the recommender systems pipeline requires design decisions that influence the performance of the recommender system. To ease design decisions, automated machine learning (AutoML) techniques have been adapted to the field of recommender systems, resulting in various AutoRecSys libraries. Nevertheless, these libraries limit flexibility in integrating automation techniques. In response, our research aims to enhance the usability of AutoML techniques for design decisions in recommender system pipelines. We focus on developing flexible and library-independent automation techniques for algorithm selection, model selection, and postprocessing steps. By enabling developers to make informed choices and ease the recommender system development process, we decrease the developer’s effort while improving the performance of the recommender systems. Moreover, we want to analyze the cost-to-benefit ratio of automation techniques in recommender systems, evaluating the computational overhead and the resulting improvements in predictive performance. Our objective is to leverage AutoML concepts to automate design decisions in recommender system pipelines, reduce manual effort, and enhance the overall performance and usability of recommender systems.
Workshop on Recommenders in Tourism (RecTour) 2023

The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topic areas of RecTour submissions. These include context-aware recommendations, group recommender systems, recommending composite items, decision making and user interaction issues, different information sources and various application scenarios.
ORSUM 2023 - 6th Workshop on Online Recommender Systems and User Modeling

Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data. Some of this data has to be kept to later be used in batches to train personalization models. However, since user activity data can be generated at very fast rates it is also useful to have algorithms able to process data streams online, in real time. Given the continuous and potentially fast change of content, context and user preferences or intents, stream-based models, and their synchronization with batch models can be extremely challenging. Therefore, it is important to investigate methods able to transparently and continuously adapt to the inherent dynamics of user interactions, preferably over long periods of time. Models able to continuously learn from such flows of data are gaining attention in the recommender systems community, and are being increasingly deployed in online platforms. However, many challenges associated with learning from streams need further investigation.
Accelerating Creator Audience Building through Centralized Exploration

On Spotify, multiple recommender systems enable personalized user experiences across a wide range of product features. These systems are owned by different teams and serve different goals, but all of these systems need to explore and learn about new content as it appears on the platform. In this work, we describe ongoing efforts at Spotify to develop an efficient solution to this problem, by centralizing content exploration and providing signals to existing, decentralized recommendation systems (a.k.a. exploitation systems). We take a creator-centric perspective, and argue that this approach can dramatically reduce the time it takes for new content to reach its full potential.
Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models

Recommender Systems (RSs) play a pivotal role in delivering personalized recommendations across various domains, from e-commerce to content streaming platforms. Recent advancements in natural language processing have introduced Large Language Models (LLMs) that exhibit remarkable capabilities in understanding and generating human-like text. RS are renowned for their effectiveness and proficiency within clearly defined domains; nevertheless, they are limited in adaptability and incapable of providing recommendations for unexplored data. Conversely, LLMs exhibit contextual awareness and strong adaptability to unseen data. Combining these technologies creates a powerful tool for delivering contextual and relevant recommendations, even in cold scenarios characterized by high data sparsity. The proposal aims to explore the possibilities of integrating LLMs into RS, introducing a novel approach called Retrieval-augmented Recommender Systems, which combines the strengths of retrieval-based and generation-based models to enhance the ability of RSs to provide relevant suggestions.
Automating the Detection of Fraudulent Activities in Online Banking Service

Banks offering Online Banking services need to detect and prevent unauthorized electronic funds transfers to reduce financial crime risk. They monitor online banking transaction histories and use their own methods to detect and prevent unauthorized electronic fund transfers. However, unauthorized electronic fund transfers by criminals have not been eliminated. The average rate of false positives in the transaction monitoring systems installed in Japanese banks is up to 99%, indicating that the monitoring systems are not fully functional. Furthermore, the personnel responsible for fraud detection must manually check a large number of false positives, making it difficult for operators to be productive in their assigned tasks. Based on the above background, we develop a method to detect unauthorized electronic fund transfers and suspicious transactions with high accuracy using machine learning algorithms and evaluate its accuracy. Specifically, a supervised machine learning algorithm is applied to detect fraudulent transactions automatically. We evaluated the proposed method on a large set of online banking transaction data provided by a major Japanese bank for the period March 2019 to May 2020. We demonstrated that our approach could detect fraudulent activity with extremely high accuracy; FPR=0.000 and FNR=0.005 can be achieved for a security policy that minimizes false positives.
A Study on Quantized Parameters for Protection of a Model and Its Inference Input

Protecting a machine learning model and its inference inputs with secure computation is important for providing services with a valuable model. In this paper, we discuss how a model's parameter quantization works to protect the model and its inference inputs. To this end, we present an investigational protocol called MOTUS, based on ternary neural networks whose parameters are ternarized. Through extensive experiments with MOTUS, we found three key insights. First, ternary neural networks can avoid deterioration in accuracy due to secure computation with modulo operations. Second, the increment of model parameter candidates significantly improves accuracy more than an existing technique for accuracy improvement, i.e., batch normalization. Third, protecting both a model and inference inputs reduces inference throughput by four to seven times to provide the same level of accuracy compared with existing protocols protecting only inference inputs. We have released our source code via GitHub.
ASRA-Q: AI Security Risk Assessment by Selective Questions

In this paper, we propose a new framework for security risk assessment. To conduct security analysis efficiently, it is necessary for developers to assess the security risks of machine learning based system (MLS) by themselves, but existing technologies cannot be used to such a purpose. Using the proposed framework, MLS developers can assess the security risks of MLSs by themselves. Our framework consists of two phases. In the preparation phase, a machine learning security expert extracts conditions of adversarial attacks for each adversarial attack method and makes an attack tree for each attack method using the extracted conditions. In addition, they prepare yes/no questions corresponding to extracted conditions. In the assessment phase, MLS developers just answer yes/no questions, and the assessment results are shown. We asked some developers to evaluate our proposal by implementing the proposed framework. As a result, they found some vulnerabilities in MLSs they chose to analyze. We received positive comments from them as results of the questionnaire.
Explainable Graph Neural Network Recommenders; Challenges and Opportunities

Graph Neural Networks (GNNs) have demonstrated significant potential in recommendation tasks by effectively capturing intricate connections among users, items, and their associated features. Given the escalating demand for interpretability, current research endeavors in the domain of GNNs for Recommender Systems (RecSys) necessitate the development of explainer methodologies to elucidate the decision-making process underlying GNN-based recommendations. In this work, we aim to present our research focused on techniques to extend beyond the existing approaches for addressing interpretability in GNN-based RecSys.
A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers

Cryptanalysis in a blackbox setting using deep learning is powerful because it does not require the attacker to have knowledge about the internal structure of the cryptographic algorithm. Thus, it is necessary to design a symmetric key cipher that is secure against cryptanalysis using deep learning. Kimura et al. (AIoTS 2022) investigated deep learning-based attacks on the small PRESENT-[4] block cipher with limited component changes, identifying characteristics specific to these attacks which remain unaffected by linear/differential cryptanalysis. Finding such characteristics is important because exploiting such characteristics can make the target cipher vulnerable to deep learning-based attacks. Thus, this paper extends a previous method to explore clues for designing symmetric-key cryptographic algorithms that are secure against deep learning-based attacks. We employ small PRESENT-[4] with two weak S-boxes, which are known to be weak against differential/linear attacks, to clarify the relationship between classical and deep learning-based attacks. As a result, we demonstrated the success probability of our deep learning-based whitebox analysis tends to be affected by the success probability of classical cryptanalysis methods. And we showed our whitebox analysis achieved the same attack capability as traditional methods even when the S-box of the target cipher was changed to a weak one.
MCM: A Multi-task Pre-trained Customer Model for Personalization

Personalization plays a critical role in helping customers discover the products and contents they prefer for e-commerce stores.Personalized recommendations differ in contents, target customers, and UI. However, they require a common core capability - the ability to deeply understand customers’ preferences and shopping intents. In this paper, we introduce the MCM (Multi-task pre-trained Customer Model), a large pre-trained BERT-based multi-task customer model with 10 million trainable parameters for e-commerce stores. This model aims to empower all personalization projects by providing commonly used preference scores for recommendations, customer embeddings for transfer learning, and a pre-trained model for fine-tuning. In this work, we improve the SOTA BERT4Rec framework to handle heterogeneous customer signals and multi-task training as well as innovate new data augmentation method that is suitable for recommendation task. Experimental results show that MCM outperforms the original BERT4Rec by 17% on on NDCG@10 of next action prediction tasks. Additionally, we demonstrate that the model can be easily fine-tuned to assist a specific recommendation task. For instance, after fine-tuning MCM for an incentive based recommendation project, performance improves by 60% on the conversion prediction task and 25% on the click-through prediction task compared to a baseline tree-based GBDT model.