Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.Comment: Accepted by EMNLP 2023 (Findings

Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems

Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side fairness issues, the investigation into how these biases affect beyond-accuracy aspects of recommendation systems - such as diversity, novelty, coverage, and serendipity - has been less emphasized. In this paper, we address this gap by introducing a simple yet effective post-processing re-ranking model that prioritizes provider fairness, while simultaneously maintaining user relevance and recommendation quality. We then conduct an in-depth evaluation of the model's impact on various aspects of recommendation quality across multiple datasets. Specifically, we apply the post-processing algorithm to four distinct recommendation models across four varied domain datasets, assessing the improvement in each metric, encompassing both accuracy and beyond-accuracy aspects. This comprehensive analysis allows us to gauge the effectiveness of our approach in mitigating provider biases. Our findings underscore the effectiveness of the adopted method in improving provider fairness and recommendation quality. They also provide valuable insights into the trade-offs involved in achieving fairness in recommender systems, contributing to a more nuanced understanding of this complex issue.Comment: FAccTRec at RecSys 202

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202

Category-Aware Location Embedding for Point-of-Interest Recommendation

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.Comment: 4 pages, 1 figure

Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (7.9 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure and heart rate associated with 70 ARDS patients admitted to five University of California academic health centers (containing 42506 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Using only the first eight days of the data, our deep learning model is able to achieve 78.79% accuracy to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients

Barriers to Family Caregivers’ Coping With Patients With Severe Mental Illness in Iran

The broad spectrum of problems caused by caring for a patient with mental illness imposes a high burden on family caregivers. This can affect how they cope with their mentally ill family members. Identifying caregivers’ experiences of barriers to coping is necessary to develop a program to help them overcome these challenges. This qualitative content analysis study explored barriers impeding family caregivers’ ability to cope with their relatives diagnosed with severe mental illness (defined here as schizophrenia, schizoaffective disorders, and bipolar affective disorders). Sixteen family caregivers were recruited using purposive sampling and interviewed using a semi-structured in-depth interview method. Data were analyzed by a conventional content analytic approach. Findings consisted of four major categories: the patient’s isolation from everyday life, incomplete recovery, lack of support by the mental health care system, and stigmatization. Findings highlight the necessity of providing support for caregivers by the mental health care delivery service system.The study was supported by Grant TBZMED·REC.5825 from the deputy of research in Tabriz University of Medical Sciences