Reviewerly/exHarmony huggingface dataset:
Adaptive learning and artificial intelligence in the educational space

Educational technologies leverage artificial intelligence and adaptive learning algorithms to analyze learners' performance and provide personalized feedback and recommendations. This helps identify areas of improvement and tailor learning experiences accordingly. Adaptive learning and artificial intelligence (AI) have emerged as powerful tools in the field of education, revolutionizing traditional teaching methods. This article explores the integration of adaptive learning technologies with AI algorithms to personalize and enhance the learning experience for students. The use of AI allows for the collection and analysis of vast amounts of data, enabling the system to adapt and tailor the content and delivery of educational materials to meet the individual needs of each student. By continuously monitoring and evaluating student performance, AI algorithms can identify areas of weakness and provide targeted interventions to address them. This personalized approach not only improves learning outcomes but also promotes student engagement and motivation. Additionally, AI-powered adaptive learning systems have the potential to assist teachers by automating administrative tasks, providing real-time feedback, and generating comprehensive progress reports. However, challenges such as privacy concerns, ethical considerations, and the need for teacher training in utilizing these technologies must be addressed. Overall, the integration of adaptive learning and AI holds great promise in transforming education by creating personalized and effective learning experiences for students.
Enhancing Chemistry Education's Relevance and Comprehension through Immersive Virtual Reality

The study is focused on evaluating the effectiveness of integrating virtual reality (VR) with the principles of chemistry education (CE) in order to improve students' understanding of chemistry. A mixed method approach, including literature review, empirical research, and analysis of VR sessions, was used to identify the impact of VR on students' conceptual understanding and motivation. The results confirmed that VR integration significantly increases conceptual understanding and student engagement. VR provides interactive and practice-oriented learning, breaking the abstraction of chemical concepts. The approach also highlights the need to rethink traditional pedagogical methods in favor of active, contextualized learning. Emerging issues, including equity of access and job training, require attention to successfully integrate VR into education.
Distinguishing AI- and Human-Generated Code: A Case Study

While the use of AI assistants for code generation has the potential to revolutionize the way software is produced, assistants may generate insecure code, either by accident or as a result of poisoning attacks. They may also inadvertently violate copyright laws by mimicking code protected by restrictive licenses. We argue for the importance of tracking the provenance of AI-generated code in the software supply chain, so that adequate controls can be put in place to mitigate risks. For that, it is necessary to have techniques that can distinguish between human- and AI-generate code, and we conduct a case study in regards to whether such techniques can reliably work. We evaluate the effectiveness of lexical and syntactic features for distinguishing AI- and human-generated code on a standardized task. Results show accuracy up to 92%, suggesting that the problem deserves further investigation.
Multimodal Fashion Knowledge Extraction as Captioning

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is classification-based and requires to manually determine a set of fashion knowledge categories in advance. In our work, we propose to cast the task as a captioning problem to capture the interplay of the multimodal post information. Specifically, we transform the fashion knowledge tuples into a natural language caption with a sentence transformation method. Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post. Inspired by the big success of pre-trained models, we build our model based on a multimodal pre-trained generative model and design several auxiliary tasks for enhancing the knowledge extraction. Since there is no existing dataset which can be directly borrowed to our task, we introduce a dataset consisting of social media posts with manual fashion knowledge annotation. Extensive experiments are conducted to demonstrate the effectiveness of our model.
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction

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.
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval

Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search engines. The main issue is that the pre-trained language model-based encoders used by DRs are typically trained and fine-tuned using clean, well-curated text data. Misspelled queries are typically not found in the data used for training these models, and thus misspelled queries observed at inference time are out-of-distribution compared to the data used for training and fine-tuning. Previous efforts to address this issue have focused on fine-tuning strategies, but their effectiveness on misspelled queries remains lower than that of pipelines that employ separate state-of-the-art spell-checking components.
RFR: Representation-Focused Replay for Overcoming the Catastrophic Forgetting in Lifelong Language Learning

Replay-based approaches can combine with regularization-based approaches by introducing additional regularization terms or optimization constraints to alleviate catastrophic forgetting in lifelong language learning. The typical approach usually penalizes changes in the mapping function of a neural network. However, this constraint requires that the parameters of the whole network space are restricted when the model is learning the new tasks. It requires that the solution space of the model can only be near the original solution space, which limits the learning ability of the model on new data. To address this issue, we propose a novel approach called the Representation-Focused Replay (RFR) approach for lifelong language learning. RFR opts to prevent the changes of representations of replay examples while training with new data by introducing the differences in representations as the optimization constraint. Extensive experiments conducted on text classification benchmarks demonstrate the effectiveness of our proposed method. The experimental results show that RFR achieves an average accuracy of 78.2. Compared with the state-of-the-art baselines, RFR achieves higher accuracy on some task sequences and is close to the upper bound for the multi-task learning method.
AdaReX: Cross-Domain, Adaptive, and Explainable Recommender System

Explainability is an inherent issue of recommender systems and has received a lot of attention recently. Generative explainable recommendation, which provides personalized explanations by generating textual rationales, is emerging as an effective solution. Despite promising, current methods face limitations in their reliance on dense training data, which hinders the generalizability of explainable recommender systems. Our work tackles a novel problem of cross-domain explainable recommendation aiming to extend the generalizability of explainable recommender systems. To solve this, we propose a novel approach that models aspects extracted from past reviews, to empower the explainable recommender systems by leveraging knowledge from other domains. Specifically, we propose AdaReX (Adaptive eXplainable Recommendation), to model auxiliary and target domains simultaneously. By performing specific tasks in respective domains and their interconnection via a discriminator model, AdaReX allows the aspect sequences to learn common knowledge across different domains and tasks. Furthermore, through our proposed optimization objective, the learning of aspect sequence is deeply cross-interacted with in-domain users and items’ latent factors, enabling the enhanced sharing of knowledge between domains. Our extensive experiments on real datasets demonstrate that our approach not only generates better explanations and recommendations for sparse users but also improves performance for general users.
Unbiased Top-$k$ Learning to Rank with Causal Likelihood Decomposition

Unbiased learning to rank methods have been proposed to address biases in search ranking. These biases, known as position bias and sample selection bias, often occur simultaneously in real applications. Existing approaches either tackle these biases separately or treat them as identical, leading to incomplete elimination of both biases. This paper employs a causal graph approach to investigate the mechanisms and interplay between position bias and sample selection bias. The analysis reveals that position bias is a common confounder bias, while sample selection bias falls under the category of collider bias. These biases collectively introduce a cascading process that leads to biased clicks. Based on our analysis, we propose Causal Likelihood Decomposition (CLD), a unified method that effectively mitigates both biases in top-k learning to rank. CLD removes position bias by leveraging propensity scores and then decomposes the likelihood of selection biased data into sample selection bias term and relevance term. By maximizing the overall log-likelihood function, we obtain an unbiased ranking model from the relevance term. We also extend CLD to pairwise neural ranking. Extensive experiments demonstrate that CLD and its pairwise neural extension outperform baseline methods by effectively mitigating both position bias and sample selection bias. The robustness of CLD is further validated through empirical studies considering variations in bias severity and click noise.
Towards Sequential Counterfactual Learning to Rank

Counterfactual evaluation plays a crucial role in learning-to-rank problems, as it addresses the discrepancy between the data logging policy and the policy being evaluated, due to the presence of presentation bias. Existing counterfactual methods, which are based on the empirical risk minimization framework, aim to evaluate the ability of a ranking policy to produce optimal results for a single query using implicit feedback from logged data. In real-world scenarios, however, users often reformulate their queries multiple times until they find what they are looking for and then provide a feedback signal. In such circumstances, current counterfactual approaches cannot assess a policy’s effectiveness in delivering satisfactory results to the user over consecutive queries during a search session. Taking sequential search behavior into account, we propose the first counterfactual estimator for session ranking metrics under sequential position-based models and conduct preliminary experiments to shed light on further research in this direction.
Selecting which Dense Retriever to use for Zero-Shot Search

We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i.e. in a zero-shot setting. Many dense retrieval models are readily available. Each model however is characterized by very differing search effectiveness – not just on the test portion of the datasets in which the dense representations have been learned but, importantly, also across different datasets for which data was not used to learn the dense representations. This is because dense retrievers typically require training on a large amount of labeled data to achieve satisfactory search effectiveness in a specific dataset or domain. Moreover, effectiveness gains obtained by dense retrievers on datasets for which they are able to observe labels during training, do not necessarily generalise to datasets that have not been observed during training.
User-Meal Interaction Learning for Meal Recommendation: A Reproducibility Study

Recommendation systems are important in web3.0 as a technology to achieve high-quality interaction between people and the web. Meal recommender system, as an application of bundle recommendation, aims to provide courses from specific categories (e.g., appetizer, main dish) that are enjoyed as a meal for a user. Common methods include collaborative filtering, attention-based and graph-based ones, etc. User-meal interaction learning is the core of a meal recommendation and its key feature is category-constrained. However, there is no work to compare and study different types of methods. Moreover, existing work does not make full use of category information in the meal recommendation. In this paper, we conduct a reproducibility study about user-meal interaction learning for meal recommendation. We reproduce seven state-of-the-art meal and general bundle recommendation models and re-implement two models with considering category information. Extensive experiments are conducted on two datasets with different user-meal interaction densities to explore the impact of data density on different types of user-meal interaction learning, and investigate the effectiveness of different category-wise implementations. The experimental results are instructive and beneficial to the development and application of meal recommendation research.
Lossy Compression Options for Dense Index Retention

Dense indexes derived from whole-of-document neural models are now more effective at locating likely-relevant documents than are conventional term-based inverted indexes. That effectiveness comes at a cost, however: inverted indexes require less than a byte per posting to store, whereas dense indexes store a fixed-length vector of floating point coefficients (typically 768) for each document, making them potentially an order of magnitude larger. In this paper we consider compression of indexes employing dense vectors. Only limited space savings can be achieved via lossless compression techniques, but we demonstrate that dense indexes are responsive to lossy techniques that sacrifice controlled amounts of numeric resolution in order to gain compressibility. We describe suitable schemes, and, via experiments on three different collections, show that substantial space savings can be achieved with minimal loss of ranking fidelity. These techniques further boost the attractiveness of dense indexes for practical use.
Enhancing Sparse Retrieval via Unsupervised Learning

Recent work has shown that neural retrieval models excel at text ranking tasks in a supervised setting when given large amounts of manually labeled training data. However, it remains an open question how to train unsupervised retrieval models that are more effective than baselines such as BM25. While some progress has been made in unsupervised dense retrieval models within a bi-encoder architecture, unsupervised sparse retrieval models remain unexplored. We propose BM26, to our knowledge the first such model, which is trained in an unsupervised manner without the need for any human relevance judgments. Evaluations with multiple test collections show that BM26 performs on par with BM25 and outperforms Contriever, the current state-of-the-art unsupervised dense retriever. We further demonstrate two promising avenues to enhance lexical retrieval: First, we can combine BM25 and BM26 using simple vector concatenation to yield an unsupervised hybrid BM51 model that significantly improves over BM25 alone. Second, we can enhance supervised sparse models such as SPLADE with improved initialization using BM26, yielding significant improvements in in-domain and zero-shot retrieval effectiveness.
Examination of Information Problem Decomposition Strategies: A New Perspective for Understanding Users' Information Problems in Search as Learning

When searching for information in search as learning (SAL), users with anomalous state of knowledge often encounter the difficulty in clearly clarifying their information needs. However, it might be easier for them to express their information problems rather than information needs. In this study, we proposed the concept of “information problem decomposition strategies (IPDS)” to help searchers explicitly express their information problems (IPs) as well as to facilitate their information problem solving (IPS) process. To unfold users’ dynamic cognitive process within information problem decomposition process during search, we conducted an experiment, and collected searchers’ sub-information problems through pre-search and post-search questionnaires, experiment recordings, and post-experiment interview. Furthermore, we also proposed a new measurement of learning performance, information problem solving degree, to evaluate how each sub-information problem has been solved during search in order to assess the search performance from the process perspective.
Sequential Recommendation with User Evolving Preference Decomposition

Modeling user sequential behaviors has recently attracted increasing attention in the recommendation domain. Existing methods mostly assume coherent preference in the same sequence. However, user personalities are volatile and easily changed, and there can be multiple mixed preferences underlying user behaviors. To solve this problem, in this paper, we propose a novel sequential recommender model via decomposing and modeling user independent preferences. To achieve this goal, we highlight three practical challenges considering the inconsistent, evolving and uneven nature of the user behaviors. For overcoming these challenges in a unified framework, we introduce a reinforcement learning module to simulate the evolution of user preference. More specifically, the action aims to allocate each item into a sub-sequence or create a new one according to how the previous items are decomposed as well as the time interval between successive behaviors. The reward is associated with the final loss of the learning objective, aiming to generate sub-sequences which can better fit the training data. We conduct extensive experiments based on eight real-world datasets across different domains. Comparing with the state-of-the-art methods, empirical studies manifest that our model can on average improve the performance by about 9.68%, 12.4%, 8.56% and 7.13% on the metrics of Precision, Recall, NDCG and MRR, respectively.
Recent Advances in Generative Information Retrieval

Generative retrieval (GR) has become a highly active area of information retrieval (IR) that has witnessed significant growth recently. Compared to the traditional “index-retrieve-then-rank” pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and the applications of GR. We end by outlining remaining challenges and issuing a call for future GR research. This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.
Toward a model to enhance the applicability of blockchain in maritime shipping: a qualitative study from the Middle East

Purpose Existing literature is still lacking field works that reflect the implications and applications of blockchain in supply chain management. This paper aims to explore the role of blockchain technology in improving the performance of maritime shipping and develop a model to enhance blockchain applicability. Design/methodology/approach Qualitative data were collected through 28 semi-structured interviews from several supply chain actors in the Middle East and were analyzed based on a thematic analysis approach using NVivo software. Findings An emerging model for improving the performance of the maritime shipping industry through blockchain technology has been developed. The findings suggest that there are transparency and process efficiency–related improvements as an outcome of Blockchain implementation in the maritime shipping industry. Practical implications As shipping industry is largely fragmented, small players find it difficult to achieve great benefits such as those achieved by larger players in the sector. The authors’ model provides guidance for the implementation of Blockchain. Originality/value To the best of the authors’ knowledge, this is one of the first scholarly works to investigate Blockchain applicability in shipping industry in the Middle East. The lack of a universal standard is a considerable challenge which is still hindering the development of blockchain applications that integrate the different actors.