Reviewerly/exHarmony huggingface dataset:
Facebook Content Search: Efficient and Effective Adapting Search on A Large Scale

Facebook content search is a critical channel that enables people to discover the best content to deepen their engagement with friends and family, creators, and communities. Building a highly personalized search engine to serve billions of daily active users to find the best results from a large scale of candidates is a challenging task. The search engine must take multiple dimensions into consideration, including different content types, different query intents, and user social graph, etc. In this paper, we discuss the challenges of Facebook content search in depth, and then describe our novel approach to efficiently handling a massive number of documents with advanced query understanding, retrieval, and machine learning techniques. The proposed system has been fully verified and applied to the production system of Facebook Search, which serves billions of users.
HDNR: A Hyperbolic-Based Debiased Approach for Personalized News Recommendation

Personalized news recommendation aims to recommend candidate news to the target user, according to the clicked news history. The user-news interaction data exhibits power-law distribution, however, existing works usually learn representations in Euclidean space which makes inconsistent capacities between data space and embedding space, leading to severe representation distortion problem. Besides, the existence of conformity bias, a potential cause of power-law distribution, may introduce biased guidance to learn user representations. In this paper, we propose a novel debiased method based on hyperbolic space, named HDNR, to tackle the above problems. Specifically, first, we employ hyperboloid model with exponential growth capacity to conduct user and news modeling, in order to solve inconsistent space capacities problem and obtain low distortion representations. Second, we design a re-weighting aggregation module to further mitigate conformity bias in data distribution, through considering local importance of the clicked news among contextual history and its global popularity degree simultaneously. Finally, we calculate the relevance score between target user and candidate news representations. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.
CEC: Towards Learning Global Optimized Recommendation through Causality Enhanced Conversion Model

Most e-commerce platforms consist of multiple entries (e.g., recommendation, search, shopping cart and etc.) for users to purchase their liked items. Among the research on the recommendation entry, most of them focus on improving the conversion volumes merely in the recommendation entry. However, such way could not ensure an increase in the global conversion volumes of the e-commerce platform. To achieve this goal by optimizing the recommendation entry only, in this paper, we focus on modeling the causality between the recommendation-entry-impression and the conversion by proposing the two-stage Causality Enhanced Conversion (CEC) model. In the first stage, we define the recommendation-entry-impression as treatment, then we estimate the conversion rate conditioned on the inclusion or exclusion of treatment respectively and calculate the corresponding individual treatment effect (ITE). In the second stage, we propose a propensity-normalization (PN) based method to transform the learned ITE to a weight term for instance weighting in the conversion loss. Extensive offline and online experiments on a large-scale food e-commerce scenario demonstrate that the CEC model could focus more on those conversed instances that can improve the global conversion volumes of the platform.
Measuring Item Global Residual Value for Fair Recommendation

In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance.
Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks

In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (a knowledge base (KB), or a text corpus, or a set of tables), thus being unable to benefit from increased answer coverage and redundancy of multiple sources. Our method EXPLAIGNN overcomes these limitations by integrating information from a mixture of sources with user-comprehensible explanations for answers. It constructs a heterogeneous graph from entities and evidence snippets retrieved from a KB, a text corpus, web tables, and infoboxes. This large graph is then iteratively reduced via graph neural networks that incorporate question-level attention, until the best answers and their explanations are distilled. Experiments show that EXPLAIGNN improves performance over state-of-the-art baselines. A user study demonstrates that derived answers are understandable by end users.
A Data-centric Solution to Improve Online Performance of Customer Service Bots

The online performance of customer service bots is often less than satisfactory because of the gap between limited training data and real-world user questions. As a straightforward way to improve online performance, model iteration and re-deployment are time consuming and labor-intensive, and therefore difficult to sustain. To fix badcases and improve online performance of chatbots in a timely and continuous manner, we propose a data-centric solution consisting of three main modules: badcase detection, bad case correction, and answer extraction. By making full use of online model signals, implicit user feedback and artificial customer service log, the proposed solution can fix online badcases automatically. Our solution has been deployed and bringing consistently positive impacts for hundreds of customer service bots used by Alipay app.
Large-Scale Data Processing for Information Retrieval Applications

Developing Information Retrieval (IR) applications such as search engines and recommendation systems require training of models that are growing in complexity and size with immense collections of data that contain multiple dimensions (documents/items text, user profiles, and interactions). Much of the research in IR concentrates on improving the performance of ranking models; however, given the high training time and high computational resources required to improve the performance by designing new models, it is crucial to address efficiency aspects of the design and deployment of IR applications at large-scale. In my thesis, I aim to improve the training efficiency of IR applications and speed up the development phase of new models, by applying dataset distillation approaches to reduce the dataset size while preserving the ranking quality and employing efficient High-Performance Computing (HPC) solutions to increase the processing speed.
Dialect classification based on the speed and the pause of speech utterances*

In this paper, we propose an approach for dialect classification based on the speed and pause of speech utterances as well as the age and gender of the speakers.Dialect classification is one of the important techniques for speech analysis.For example, an accurate dialect classification model can potentially improve the performance of speaker or speech recognition.According to previous studies, research based on deep learning using Mel-Frequency Cepstral Coefficients (MFCC) features has been the dominant approach.We focus on the acoustic differences between regions and conduct dialect classification based on the extracted features derived from the differences.In this paper, we propose an approach of extracting underexplored additional features, namely the speed and the pauses of speech utterances along with the metadata including the age and the gender of the speakers.Experimental results show that our proposed approach results in higher accuracy, especially with the speech rate feature, compared to the method only using the MFCC features.The accuracy improved from 91.02% to 97.02% compared to the previous method that only used MFCC features, by incorporating all the proposed features in this paper.
Dense Passage Retrieval: Architectures and Augmentation Methods

The dual-encoder model is a dense retrieval architecture, consisting of two encoder models, that has surpassed traditional sparse retrieval methods for open-domain retrieval [1]. But, room exists for improvement, particularly when dense retrievers are exposed to unseen passages or queries.
Towards Trustworthy Recommender System: A Faithful and Responsible Recommendation Perspective

Recommender systems (RecSys) become increasingly prevalent in modern society, offering personalized information filtering to alleviate information overload and significantly impacting various human online activities. Machine learning-based recommendation methods have been extensively developed in recent years to achieve more accurate recommendations, with some of these approaches having been extensively deployed in industrial applications, such as the Deep Interest Network (DIN). Despite their widespread use, researchers and practitioners have highlighted various trustworthiness issues inherent in these systems, including bias and promoting polarization issues. In order to better serve users and comply with regulations pertaining to recommendation algorithms established by different countries, it is essential to consider the trustworthiness issues of recommender systems.
Benchmarking Middle-Trained Language Models for Neural Search

Middle training methods aim to bridge the gap between the Masked Language Model (MLM) pre-training and the final finetuning for retrieval. Recent models such as CoCondenser, RetroMAE, and LexMAE argue that the MLM task is not sufficient enough to pre-train a transformer network for retrieval and hence propose various tasks to do so. Intrigued by those novel methods, we noticed that all these models used different finetuning protocols, making it hard to assess the benefits of middle training. We propose in this paper a benchmark of CoCondenser, RetroMAE, and LexMAE, under the same finetuning conditions. We compare both dense and sparse approaches under various finetuning protocols and middle training on different collections (MS MARCO, Wikipedia). We use additional middle training baselines, such as a standard MLM finetuning on the retrieval collection, optionally augmented by a CLS predicting the passage term frequency. For the sparse approach, our study reveals that there is almost no statistical difference between those methods: the more effective the finetuning procedure is, the less difference there is between those models. For the dense approach, RetroMAE using MS MARCO as middle-training collection shows excellent results in almost all the settings. Finally, we show that middle training on the retrieval collection, thus adapting the language model to it, is a critical factor. Overall, a better experimental setup should be adopted to evaluate middle training methods.
LOAM: Improving Long-tail Session-based Recommendation via Niche Walk Augmentation and Tail Session Mixup

Session-based recommendation aims to predict the user's next action based on anonymous sessions without using side information. Most of the real-world session datasets are sparse and have long-tail item distribution. Although long-tail item recommendation plays a crucial role in improving user satisfaction, only a few methods have been proposed to take the long-tail session recommendation into consideration. Previous works in handling data sparsity problems are mostly limited to self-supervised learning techniques with heuristic augmentation which can ruin the original characteristic of session datasets, sequential and co-occurrences, and make noisier short sessions by dropping items and cropping sequences. We propose a novel method, LOAM, improving LOng-tail session-based recommendation via niche walk Augmentation and tail session Mixup, that alleviates popularity bias and enhances long-tail recommendation performance. LOAM consists of two modules, Niche Walk Augmentation (NWA) and Tail Session Mixup (TSM). NWA can generate synthetic sessions considering long-tail distribution which are likely to be found in original datasets, unlike previous heuristic methods, and expose a recommender model to various item transitions with global information. This improves the item coverage of recommendations. TSM makes the model more generalized and robust by interpolating sessions at the representation level. It encourages the recommender system to predict niche items with more diversity and relevance. We conduct extensive experiments with four real-world datasets and verify that our methods greatly improve tail performance while balancing overall performance.
BeamQA: Multi-hop Knowledge Graph Question Answering with Sequence-to-Sequence Prediction and Beam Search

Knowledge Graph Question Answering (KGQA) is a task that aims to answer natural language queries by extracting facts from a knowledge graph. Current state-of-the-art techniques for KGQA rely on text-based information from graph entity and relations labels, as well as external textual corpora. By reasoning over multiple edges in the graph, these can accurately rank and return the most relevant entities. However, one of the limitations of these methods is that they cannot handle the inherent incompleteness of real-world knowledge graphs and may lead to inaccurate answers due to missing edges. To address this issue, recent advances in graph representation learning have led to the development of systems that can use link prediction techniques to handle missing edges probabilistically, allowing the system to reason with incomplete information. However, existing KGQA frameworks that use such techniques often depend on learning a transformation from the query representation to the graph embedding space, which requires access to a large training dataset. We present BeamQA, an approach that overcomes these limitations by combining a sequence-to-sequence prediction model with beam search execution in the embedding space. Our model uses a pre-trained large language model and synthetic question generation. Our experiments demonstrate the effectiveness of BeamQA when compared to other KGQA methods on two knowledge graph question-answering datasets.
The effect of additional shooting task on alleviating virtual reality motion sickness

Virtual reality motion sickness (VRMS) is a key obstacle in user experience and development of virtual reality technology. Given the existed condition of technology, the adjusts on the virtual reality scene and content is convenient and low-cost way to reduce the influence of VRMS. This paper aims to investigate whether the additional task in a virtual reality content can reduce the VRMS level. We collect the EEG data and simulator sickness questionnaire from the subjects during their riding a virtual reality roll coaster with and without an additional shooting task. In final, the statistic method is used to analyze the rhythm energies and differential entropies of the EEG. The SSQ results show that the shooting task can reduce the VRMS severity. The difference of the EEG features between the shooting task and no-task is also more significant than those between different field of view, that is a further proof of the reduction effect of the additional shooting task on VRMS. This study reveals the effect of additional shooting task on the VRMS and provides a novel beginning point of research on the use of additional task to prevent VRMS.
Sine cosine algorithm with communication and quality enhancement: Performance design for engineering problems

Abstract In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However, the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space.
Visual Servoing Control Method of 3D Robot Based on 3D Point Cloud Processing

A visual servoing control method based on depth point cloud is proposed to solve the problem of pose adjustment before obstacle surmounting of a rocker arm tracked robot. By pre-processing the acquired point cloud, a positional error model is created. Combined with the robot kinematics model, the adaptive control law is designed based on Lyapunov method, and the stability of the control algorithm is proved. Simulation results show that, compared with PID control and sliding mode control algorithm, the proposed control algorithm has faster convergence speed and smoother trajectory. The robot can reach the desired pose efficiently in 13. 5s in actual testing.
Introducing an Open-source Adaptive Tutoring System to Accelerate Learning Sciences Experimentation

Learning @ Scale has embraced movements that spread access to education through open and free platforms of learning. In this tutorial, we introduce OATutor (recently published at CHI'23), the field's first free and open-source adaptive tutoring system based on ITS principles and designed for rapid experimentation. The MIT-licensed platform can be deployed to git-pages in only a few clicks and supports BKT mastery-based adaptive problem selection. We demonstrate how the system can be used to rapidly run A/B experiments, analyze the data, and publish the entire tutor, content, and analysis scripts to facilitate unprecedented ease of replication and transparency, as demonstrated in a recent study comparing ChatGPT generated hints to human-tutor hints. Our four-part tutorial will include how to add lessons to the system and link to them from assignments in a MOOC platform or LMS via LTI. The structured JSON format of the four CC BY courses worth of content released with OATutor opens up avenues for researchers to apply new and existing educational data mining and NLP techniques (e.g., KC tagging) and rapidly evaluate the impact of subsequent changes on learners.
Behavior Modeling for Point of Interest Search

With the increasing popularity of location-based services, the point-of-interest (POI) search has received considerable attention in recent years. Existing studies on POI search mostly focus on how to construct better retrieval models to retrieve the relevant POI based on query-POI matching. However, user behavior in POI search, i.e., how users examine the search engine result page (SERP), is mostly underexplored. A good understanding of user behavior is well-recognized as a key to develop effective user models and retrieval models to improve the search quality. Therefore, in this paper, we propose to investigate user behavior in POI search with a lab study in which users' eye movements and their implicit feedback on the SERP are collected. Based on the collected data, we analyze (1) query-level user behavior patterns in POI search, i.e., examination and interactions on SERP; (2) session-level user behavior patterns in POI search, i.e., query reformulation, termination of search, etc. Our work sheds light on user behavior in POI search and could potentially benefit future studies on related research topics.
Leader-Generator Net: Dividing Skill and Implicitness for Conquering FairytaleQA

Machine reading comprehension requires systems to understand the given passage and answer questions. Previous methods mainly focus on the interaction between the question and passage. However, they ignore the deep exploration of cognitive elements behind questions, such as fine-grained reading skills (this paper focuses on narrative comprehension skills) and implicitness or explicitness of the question (whether the answer can be found in the passage). Grounded in prior literature on reading comprehension, the understanding of a question is a complex process where human beings need to understand the semantics of the question, use different reading skills for different questions, and then judge the implicitness of the question. To this end, a simple but effective Leader-Generator Network is proposed to explicitly separate and extract fine-grained reading skills and the implicitness or explicitness of the question. Specifically, the proposed skill leader accurately captures the semantic representation of fine-grained reading skills with contrastive learning. And the implicitness-aware pointer-generator adaptively extracts or generates the answer based on the implicitness or explicitness of the question. Furthermore, to validate the generalizability of the methodology, we annotate a new dataset named NarrativeQA 1.1. Experiments on the FairytaleQA and NarrativeQA 1.1 show that the proposed model achieves the state-of-the-art performance (about 5% gain on Rouge-L) on the question answering task. Our annotated data and code are available at https://github.com/pengwei-iie/Leader-Generator-Net.