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"Recommendation": models, code, and papers

An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes

Mar 25, 2022
Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Maria Bielikova

The negative effects of misinformation filter bubbles in adaptive systems have been known to researchers for some time. Several studies investigated, most prominently on YouTube, how fast a user can get into a misinformation filter bubble simply by selecting wrong choices from the items offered. Yet, no studies so far have investigated what it takes to burst the bubble, i.e., revert the bubble enclosure. We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics). Then, by watching misinformation debunking content, the agents try to burst the bubbles and reach more balanced recommendation mixes. We recorded the search results and recommendations, which the agents encountered, and analyzed them for the presence of misinformation. Our key finding is that bursting of a filter bubble is possible, albeit it manifests differently from topic to topic. Moreover, we observe that filter bubbles do not truly appear in some situations. We also draw a direct comparison with a previous study. Sadly, we did not find much improvements in misinformation occurrences, despite recent pledges by YouTube.

* RecSys '21: Fifteenth ACM Conference on Recommender Systems, 2021 
* RecSys '21: Fifteenth ACM Conference on Recommender System 

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Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records

Oct 29, 2020
Hua Zheng, Ilya O. Ryzhov, Wei Xie, Judy Zhong

Comorbid chronic conditions are common among people with type 2 diabetes. We developed an Artificial Intelligence algorithm, based on Reinforcement Learning (RL), for personalized diabetes and multi-morbidity management with strong potential to improve health outcomes relative to current clinical practice. In this paper, we modeled glycemia, blood pressure and cardiovascular disease (CVD) risk as health outcomes using a retrospective cohort of 16,665 patients with type 2 diabetes from New York University Langone Health ambulatory care electronic health records in 2009 to 2017. We trained a RL prescription algorithm that recommends a treatment regimen optimizing patients' cumulative health outcomes using their individual characteristics and medical history at each encounter. The RL recommendations were evaluated on an independent subset of patients. The results demonstrate that the proposed personalized reinforcement learning prescriptive framework for type 2 diabetes yielded high concordance with clinicians' prescriptions and substantial improvements in glycemia, blood pressure, cardiovascular disease risk outcomes.

* 26 pages, 3 figures 

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Privacy-Preserving Bandits

Sep 12, 2019
Mohammad Malekzadeh, Dimitrios Athanasakis, Hamed Haddadi, Ben Livshits

Contextual bandit algorithms (CBAs) often rely on personal data to provide recommendations. This means that potentially sensitive data from past interactions are utilized to provide personalization to end-users. Using a local agent on the user's device protects the user's privacy, by keeping the data locally, however, the agent requires longer to produce useful recommendations, as it does not leverage feedback from other users. This paper proposes a technique we call Privacy-Preserving Bandits (P2B), a system that updates local agents by collecting feedback from other agents in a differentially-private manner. Comparisons of our proposed approach with a non-private, as well as a fully-private (local) system, show competitive performance on both synthetic benchmarks and real-world data. Specifically, we observed a decrease of 2.6% and 3.6% in multi-label classification accuracy, and a CTR increase of 0.0025 in online advertising for a privacy budget $\epsilon \approx$ 0.693. These results suggest P2B is an effective approach to problems arising in on-device privacy-preserving personalization.

* 9 pages, 7 figures 

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DeepSoft: A vision for a deep model of software

Jul 30, 2016
Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.

* FSE 2016 

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GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

Apr 08, 2022
Yoonseok Yang, Kyu Seok Kim, Minsam Kim, Juneyoung Park

Content-based collaborative filtering (CCF) provides personalized item recommendations based on both users' interaction history and items' content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to finetune PLM in an end-to-end (E2E) manner in CCF due to its multi-modal nature: optimization involves redundant content encoding for interactions from users. For this, we propose GRAM (GRadient Accumulation for Multi-modality): (1) Single-step GRAM which aggregates gradients for each item while maintaining theoretical equivalence with E2E, and (2) Multi-step GRAM which further accumulates gradients across multiple training steps, with less than 40\% GPU memory footprint of E2E. We empirically confirm that GRAM achieves a remarkable boost in training efficiency based on five datasets from two task domains of Knowledge Tracing and News Recommendation, where single-step and multi-step GRAM achieve 4x and 45x training speedup on average, respectively.

* NAACL 2022 Main Conference 

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Hierarchical Latent Relation Modeling for Collaborative Metric Learning

Jul 26, 2021
Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin, Manuel Moussallam

Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods learn fixed user and item representations, which fails to capture the complex interests of users. Existing extensions of CML also either ignore the heterogeneity of user-item relations, i.e. that a user can simultaneously like very different items, or the latent item-item relations, i.e. that a user's preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with. In this paper, we present a hierarchical CML model that jointly captures latent user-item and item-item relations from implicit data. Our approach is inspired by translation mechanisms from knowledge graph embedding and leverages memory-based attention networks. We empirically show the relevance of this joint relational modeling, by outperforming existing CML models on recommendation tasks on several real-world datasets. Our experiments also emphasize the limits of current CML relational models on very sparse datasets.

* 15th ACM Conference on Recommender Systems (RecSys 2021) 

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Identifying Causal Effect Inference Failure with Uncertainty-Aware Models

Jul 01, 2020
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical domains such as healthcare, where estimating and communicating uncertainty to decision-makers is crucial. We introduce a practical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applications of causal effect approaches fail. Further, our methods allow us to handle covariate shift, where test distribution differs to train distribution, common when systems are deployed in practice. We show that when such a covariate shift occurs, correctly modeling uncertainty can keep us from giving overconfident and potentially harmful recommendations. We demonstrate our methodology with a range of state-of-the-art models. Under both covariate shift and lack of overlap, our uncertainty-equipped methods can alert decisions makers when predictions are not to be trusted while outperforming their uncertainty-oblivious counterparts.


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SupRB: A Supervised Rule-based Learning System for Continuous Problems

Feb 24, 2020
Michael Heider, David Pätzel, Jörg Hähner

We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.

* Submitted to the Genetic and Evolutionary Computation Conference 2020 (GECCO 2020) 

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Product Knowledge Graph Embedding for E-commerce

Nov 28, 2019
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.


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Improving Contrastive Learning with Model Augmentation

Mar 25, 2022
Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong

The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity and noise issues in sequences, a new self-supervised learning (SSL) paradigm is proposed to improve the performance, which employs contrastive learning between positive and negative views of sequences. However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals. Therefore, we investigate the possibility of model augmentation to construct view pairs. We propose three levels of model augmentation methods: neuron masking, layer dropping, and encoder complementing. This work opens up a novel direction in constructing views for contrastive SSL. Experiments verify the efficacy of model augmentation for the SSL in the sequential recommendation. Code is available\footnote{\url{https://github.com/salesforce/SRMA}}.

* Preprint. Still under reivew 

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