pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting BPR: A Replicability Study of a Common Recommender System Baseline
Authors: Aleksandr Milogradskii, Oleg Lashinin, Alexander P, Marina Ananyeva, Sergey Kolesnikov
Date: 2024-09-21 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Bayesian Personalized Ranking (BPR), a collaborative filtering approach based on matrix factorization, frequently serves as a benchmark for recommender systems research. However, numerous studies often overlook the nuances of BPR implementation, claiming that it performs worse than newly proposed methods across various tasks. In this paper, we thoroughly examine the features of the BPR model, indicating their impact on its performance, and investigate open-source BPR implementations. Our analysis reveals inconsistencies between these implementations and the original BPR paper, leading to a significant decrease in performance of up to 50% for specific implementations. Furthermore, through extensive experiments on real-world datasets under modern evaluation settings, we demonstrate that with proper tuning of its hyperparameters, the BPR model can achieve performance levels close to state-of-the-art methods on the top-n recommendation tasks and even outperform them on specific datasets. Specifically, on the Million Song Dataset, the BPR model with hyperparameters tuning statistically significantly outperforms Mult-VAE by 10% in NDCG@100 with binary relevance function.
Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
Authors: Swanand Joshi, Yesu Feng, Ko-Jen Hsiao, Zhe Zhang, Sudarshan Lamkhede
Date: 2024-08-21 00:00:00
Tasks: Recommendation Systems

Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys foundation models (FM) need to encode this information in pretraining. Usually, this is done by either generating a long enough sequence length to take all history sequences as input at the cost of large model input dimension or by dropping some parts of the user history to accommodate model size and latency requirements on the production serving side. In this paper, we introduce a sliding window training technique to incorporate long user history sequences during training time without increasing the model input dimension. We show the quantitative & qualitative improvements this technique brings to the RecSys FM in learning user long term preferences. We additionally show that the average quality of items in the catalog learnt in pretraining also improves.
tabulapdf: An R Package to Extract Tables from PDF Documents
Authors: Mauricio Vargas Sepúlveda, Thomas J. Leeper, Tom Paskhalis, Manuel Aristarán, Jeremy B. Merrill, Mike Tigas
Date: 2024-08-25 00:00:00
Tasks: Retrieval, Table Extraction

tabulapdf is an R package that utilizes the Tabula Java library to import tables from PDF files directly into R. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a Shiny interface, enabling manual areas selection with a computer mouse for data retrieval.
Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems
Authors: Matthew Kolodner, Mingxuan Ju, Zihao Fan, Tong Zhao, Elham Ghazizadeh, Yan Wu, Neil Shah, Yozen Liu
Date: 2024-09-23 00:00:00
Tasks: Recommendation Systems, Retrieval

Improving recommendation systems (RS) can greatly enhance the user experience across many domains, such as social media. Many RS utilize embedding-based retrieval (EBR) approaches to retrieve candidates for recommendation. In an EBR system, the embedding quality is key. According to recent literature, self-supervised multitask learning (SSMTL) has showed strong performance on academic benchmarks in embedding learning and resulted in an overall improvement in multiple downstream tasks, demonstrating a larger resilience to the adverse conditions between each downstream task and thereby increased robustness and task generalization ability through the training objective. However, whether or not the success of SSMTL in academia as a robust training objectives translates to large-scale (i.e., over hundreds of million users and interactions in-between) industrial RS still requires verification. Simply adopting academic setups in industrial RS might entail two issues. Firstly, many self-supervised objectives require data augmentations (e.g., embedding masking/corruption) over a large portion of users and items, which is prohibitively expensive in industrial RS. Furthermore, some self-supervised objectives might not align with the recommendation task, which might lead to redundant computational overheads or negative transfer. In light of these two challenges, we evaluate using a robust training objective, specifically SSMTL, through a large-scale friend recommendation system on a social media platform in the tech sector, identifying whether this increase in robustness can work at scale in enhancing retrieval in the production setting. Through online A/B testing with SSMTL-based EBR, we observe statistically significant increases in key metrics in the friend recommendations, with up to 5.45% improvements in new friends made and 1.91% improvements in new friends made with cold-start users.
EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs
Authors: Utkarsh Priyam, Hemit Shah, Edoardo Botta
Date: 2024-09-23 00:00:00
Tasks: Collaborative Filtering, Denoising, Image Generation, Recommendation Systems

Most recommender systems research focuses on binary historical user-item interaction encodings to predict future interactions. User features, item features, and interaction strengths remain largely under-utilized in this space or only indirectly utilized, despite proving largely effective in large-scale production recommendation systems. We propose a new attention mechanism, loosely based on the principles of collaborative filtering, called Row-Column Separable Attention RCSA to take advantage of real-valued interaction weights as well as user and item features directly. Building on this mechanism, we additionally propose a novel Graph Diffusion Transformer GDiT architecture which is trained to iteratively denoise the weighted interaction matrix of the user-item interaction graph directly. The weighted interaction matrix is built from the bipartite structure of the user-item interaction graph and corresponding edge weights derived from user-item rating interactions. Inspired by the recent progress in text-conditioned image generation, our method directly produces user-item rating predictions on the same scale as the original ratings by conditioning the denoising process on user and item features with a principled approach.
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Authors: Li Li, Mingyue Cheng, Zhiding Liu, Hao Zhang, Qi Liu, Enhong Chen
Date: 2024-09-23 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Recommendation Systems, Sequential Recommendation

Sequential recommendation models user interests based on historical behaviors to provide personalized recommendation. Previous sequential recommendation algorithms primarily employ neural networks to extract features of user interests, achieving good performance. However, due to the recommendation system datasets sparsity, these algorithms often employ small-scale network frameworks, resulting in weaker generalization capability. Recently, a series of sequential recommendation algorithms based on large pre-trained language models have been proposed. Nonetheless, given the real-time demands of recommendation systems, the challenge remains in applying pre-trained language models for rapid recommendations in real scenarios. To address this, we propose a sequential recommendation algorithm based on a pre-trained language model and knowledge distillation. The key of proposed algorithm is to transfer pre-trained knowledge across domains and achieve lightweight inference by knowledge distillation. The algorithm operates in two stages: in the first stage, we fine-tune the pre-trained language model on the recommendation dataset to transfer the pre-trained knowledge to the recommendation task; in the second stage, we distill the trained language model to transfer the learned knowledge to a lightweight model. Extensive experiments on multiple public recommendation datasets show that the proposed algorithm enhances recommendation accuracy and provide timely recommendation services.
FedSlate:A Federated Deep Reinforcement Learning Recommender System
Authors: Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Yaochu Jin
Date: 2024-09-23 00:00:00
Tasks: Deep Reinforcement Learning, Federated Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning

Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems do not fully exploit the relevance of individual user behavior across different platforms. One potential solution is to aggregate data from various platforms in a centralized location and use the aggregated data for training. However, this approach raises economic and legal concerns, including increased communication costs and potential threats to user privacy. To address these challenges, we propose \textbf{FedSlate}, a federated reinforcement learning recommendation algorithm that effectively utilizes information that is prohibited from being shared at a legal level. We employ the SlateQ algorithm to assist FedSlate in learning users' long-term behavior and evaluating the value of recommended content. We extend the existing application scope of recommendation systems from single-user single-platform to single-user multi-platform and address cross-platform learning challenges by introducing federated learning. We use RecSim to construct a simulation environment for evaluating FedSlate and compare its performance with state-of-the-art benchmark recommendation models. Experimental results demonstrate the superior effects of FedSlate over baseline methods in various environmental settings, and FedSlate facilitates the learning of recommendation strategies in scenarios where baseline methods are completely inapplicable. Code is available at \textit{https://github.com/TianYaDY/FedSlate}.
EMERS: Energy Meter for Recommender Systems
Authors: Lukas Wegmeth, Tobias Vente, Alan Said, Joeran Beel
Date: 2024-09-23 00:00:00
Tasks: Recommendation Systems

Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems community often does not report the energy consumption of their experiments. In today's research landscape, no tools exist to easily measure the energy consumption of recommender systems experiments. To bridge this gap, we introduce EMERS, the first software library that simplifies measuring, monitoring, recording, and sharing the energy consumption of recommender systems experiments. EMERS measures energy consumption with smart power plugs and offers a user interface to monitor and compare the energy consumption of recommender systems experiments. Thereby, EMERS improves sustainability awareness and simplifies self-reporting energy consumption for recommender systems practitioners and researchers.
Don't Use LLMs to Make Relevance Judgments
Authors: Ian Soboroff
Date: 2024-09-23 00:00:00

Making the relevance judgments for a TREC-style test collection can be complex and expensive. A typical TREC track usually involves a team of six contractors working for 2-4 weeks. Those contractors need to be trained and monitored. Software has to be written to support recording relevance judgments correctly and efficiently. The recent advent of large language models that produce astoundingly human-like flowing text output in response to a natural language prompt has inspired IR researchers to wonder how those models might be used in the relevance judgment collection process. At the ACM SIGIR 2024 conference, a workshop ``LLM4Eval'' provided a venue for this work, and featured a data challenge activity where participants reproduced TREC deep learning track judgments, as was done by Thomas et al (arXiv:2408.08896, arXiv:2309.10621). I was asked to give a keynote at the workshop, and this paper presents that keynote in article form. The bottom-line-up-front message is, don't use LLMs to create relevance judgments for TREC-style evaluations.
Recommendation with Generative Models
Authors: Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, Rene Vidal, Maheswaran Sathiamoorthy, Atoosa Kasrizadeh, Silvia Milano, Francesco Ricci
Date: 2024-09-18 00:00:00
Tasks: Image Generation, Navigate, Recommendation Systems

Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions. In recent years, these models have gained prominence in machine learning due to the development of approaches such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures such as GPT. These models have applications across various domains, such as image generation, text synthesis, and music composition. In recommender systems, generative models, referred to as Gen-RecSys, improve the accuracy and diversity of recommendations by generating structured outputs, text-based interactions, and multimedia content. By leveraging these capabilities, Gen-RecSys can produce more personalized, engaging, and dynamic user experiences, expanding the role of AI in eCommerce, media, and beyond. Our book goes beyond existing literature by offering a comprehensive understanding of generative models and their applications, with a special focus on deep generative models (DGMs) and their classification. We introduce a taxonomy that categorizes DGMs into three types: ID-driven models, large language models (LLMs), and multimodal models. Each category addresses unique technical and architectural advancements within its respective research area. This taxonomy allows researchers to easily navigate developments in Gen-RecSys across domains such as conversational AI and multimodal content generation. Additionally, we examine the impact and potential risks of generative models, emphasizing the importance of robust evaluation frameworks.
Segment Discovery: Enhancing E-commerce Targeting
Authors: Qiqi Li, Roopali Singh, Charin Polpanumas, Tanner Fiez, Namita Kumar, Shreya Chakrabarti
Date: 2024-09-20 00:00:00

Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based on the propensity of desirable behavior. However, such policies can be suboptimal as they do not target the set of customers who would benefit the most from the intervention and they may also not take account of any constraints. In this paper, we propose a policy framework based on uplift modeling and constrained optimization that identifies customers to target for a use-case specific intervention so as to maximize the value to the business, while taking account of any given constraints. We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation.
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System
Authors: Zhenyu Zhao, Yexi Jiang
Date: 2024-09-20 00:00:00
Tasks: feature selection, Multi-Armed Bandits, Recommendation Systems

Features (a.k.a. context) are critical for contextual multi-armed bandits (MAB) performance. In practice of large scale online system, it is important to select and implement important features for the model: missing important features can led to sub-optimal reward outcome, and including irrelevant features can cause overfitting, poor model interpretability, and implementation cost. However, feature selection methods for conventional machine learning models fail short for contextual MAB use cases, as conventional methods select features correlated with the outcome variable, but not necessarily causing heterogeneuous treatment effect among arms which are truely important for contextual MAB. In this paper, we introduce model-free feature selection methods designed for contexutal MAB problem, based on heterogeneous causal effect contributed by the feature to the reward distribution. Empirical evaluation is conducted based on synthetic data as well as real data from an online experiment for optimizing content cover image in a recommender system. The results show this feature selection method effectively selects the important features that lead to higher contextual MAB reward than unimportant features. Compared with model embedded method, this model-free method has advantage of fast computation speed, ease of implementation, and prune of model mis-specification issues.
Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks
Authors: Yi Liu, Xiaoan Tang, Witold Pedrycz, Qiang Zhang
Date: 2024-09-21 00:00:00

Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applications usually have high requirements on the balance between time complexity and accuracy of influential nodes identification. To address the challenges of imperfect node influence measurement and inefficient seed nodes selection mechanism in such class of foregoing techniques, this article proposes a novel approach called Cost-Effective Community-Hierarchy-Based Mutual Voting for influence maximization in complex networks. First, we develop a method for measuring the importance of different nodes in networks based on an original concept of Dual-Scale Community-Hierarchy Information that synthesizes both hierarchy structural information and community structural information of nodes. The community structural information contained in the nodes is measured by a new notion of Hierarchical-Community Entropy. Second, we develop a method named Cost-Effective Mutual-Influence-based Voting for seed nodes selection. Hereinto, a low-computational-cost mutual voting mechanism and an updating strategy called Lazy Score Updating Strategy are newly constructed for optimizing the selecting of seed nodes. Third, we develop a balance index to evaluate the performance of different methods in striking the tradeoff between time complexity and the accuracy of influential nodes identification. Finally, we demonstrate the approach performance over ten public datasets. The extensive experiments show that the proposed approach outperforms 16 state-of-the-art techniques on the balance between time complexity and accuracy of influential nodes identification. Compared with the method with the second highest value of the balance index, our approach can be improved by at most 9.29%.
Nirjas: An open source framework for extracting metadata from the source code
Authors: Ayush Bhardwaj, Sahil, Kaushlendra Pratap, Gaurav Mishra
Date: 2024-09-22 00:00:00

Metadata and comments are critical elements of any software development process. In this paper, we explain how metadata and comments in source code can play an essential role in comprehending software. We introduce a Python-based open-source framework, Nirjas, which helps in extracting this metadata in a structured manner. Various syntaxes, types, and widely accepted conventions exist for adding comments in source files of different programming languages. Edge cases can create noise in extraction, for which we use Regex to accurately retrieve metadata. Non-Regex methods can give results but often miss accuracy and noise separation. Nirjas also separates different types of comments, source code, and provides details about those comments, such as line number, file name, language used, total SLOC, etc. Nirjas is a standalone Python framework/library and can be easily installed via source or pip (the Python package installer). Nirjas was initially created as part of a Google Summer of Code project and is currently developed and maintained under the FOSSology organization.
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Authors: Xiaoyu Tan, Yongxin Deng, Chao Qu, Siqiao Xue, Xiaoming Shi, James Zhang, Xihe Qiu
Date: 2024-09-23 00:00:00
Tasks: Marketing, Representation Learning, Segmentation

Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user representation as the input for subsequent scenario-specific models. However, in numerous industrial applications (e.g., recommendation and marketing), the business always operates such applications as various online activities among different user segmentation. These segmentation are always created by domain experts. Due to the difference in user distribution (i.e., user segmentation) and business objectives in subsequent tasks, learning solely on universal representation may lead to detrimental effects on both model performance and robustness. In this paper, we propose a novel learning framework that can first learn general universal user representation through information bottleneck. Then, merge and learn a segmentation-specific or a task-specific representation through neural interaction. We design the interactive learning process by leveraging a bipartite graph architecture to model the representation learning and merging between contextual clusters and each user segmentation. Our proposed method is evaluated in two open-source benchmarks, two offline business datasets, and deployed on two online marketing applications to predict users' CVR. The results demonstrate that our method can achieve superior performance and surpass the baseline methods.