Reviewerly/exHarmony huggingface dataset:
Towards Robust Fairness-aware Recommendation

Due to the progressive advancement of trustworthy machine learning algorithms, fairness in recommender systems is attracting increasing attention and is often considered from the perspective of users. Conventional fairness-aware recommendation models assume that user preferences remain the same between the training set and the testing set. However, this assumption is arguable in reality, where user preference can shift in the testing set due to the natural spatial or temporal heterogeneity. It is concerning that conventional fairness-aware models may be unaware of such distribution shifts, leading to a sharp decline in the model performance. To address the distribution shift problem, we propose a robust fairness-aware recommendation framework based on Distributionally Robust Optimization (DRO) technique. In specific, we assign learnable weights for each sample to approximate the distributions that leads to the worst-case model performance, and then optimize the fairness-aware recommendation model to improve the worst-case performance in terms of both fairness and recommendation accuracy. By iteratively updating the weights and the model parameter, our framework can be robust to unseen testing sets. To ease the learning difficulty of DRO, we use a hard clustering technique to reduce the number of learnable sample weights. To optimize our framework in a full differentiable manner, we soften the above clustering strategy. Empirically, we conduct extensive experiments based on four real-world datasets to verify the effectiveness of our proposed framework.
RecSys Challenge 2023: Deep Funnel Optimization with a Focus on User Privacy

The RecSys 2023 Challenge involved a conversion prediction task in the online advertising space. The dataset was provided by ShareChat (Mohalla Tech Pvt Ltd). The challenge data represents a sample of ad impressions served to the users over a period of 22 days and the task is for a given ad impression, to predict a conversion (install an app) will happen or not. The challenge ran for 3 months with a public dashboard. There were 519 teams registered and 231 teams made at least one submission. The task setting represents an important research area of modeling ad recommendations under user privacy. We identify interesting themes in feature engineering, addressing sparsity and calibrating across multi-step predictions.
Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServices

The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members’ needs and concerns while providing recommendations that satisfy all members at once. In this demonstration, we show how we implemented a Multi-Agent Microservice to model the tourists in a mobile Group Recommender System for Tourism prototype and a novel dynamic clustering process to help minimize the group's heterogeneity and conflicting preferences. To help solve the cold-start problem, the preliminary tourist attractions preference and travel-related preferences & concerns are predicted using the tourists' personality, considering the tourists’ disabilities and fears/phobias. Although there is no need for data from previous interactions to build the tourists’ profile since we predict the tourists’ preferences, the tourist agents learn with each other by using association rules to find patterns in the tourists' profile and in the ratings given to Points of Interest to refine the recommendations.
How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective

Multi-list interfaces are widely used in recommender systems, especially in industry, showing collections of recommendations, one below the other, with items that have certain commonalities. The composition and order of these "carousels" are usually optimized by simulating user interaction based on probabilistic models learned from item click data. Research that actually involves users is rare, with only few studies investigating general user experience in comparison to conventional recommendation lists. Hence, it is largely unknown how specific design aspects such as carousel type and length influence the individual perception and usage of carousel-based interfaces. This paper seeks to fill this gap through an exploratory user study. The results confirm previous assumptions about user behavior and provide first insights into the differences in decision making in the presence of multiple recommendation carousels.
DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender

Cross-Domain Sequential Recommendation(CDSR) aims to generate accurate predictions for future interactions by leveraging users' cross-domain historical interactions. One major challenge of CDSR is how to jointly learn the single- and cross-domain user preferences efficiently. To enhance the target domain's performance, most existing solutions start by learning the single-domain user preferences within each domain and then transferring the acquired knowledge from the rich domain to the target domain. However, this approach ignores the inter-sequence item relationship and also limits the opportunities for target domain knowledge to enhance the rich domain performance. Moreover, it also ignores the information within the cross-domain sequence. Despite cross-domain sequences being generally noisy and hard to learn directly, they contain valuable user behavior patterns with great potential to enhance performance. Another key challenge of CDSR is data sparsity, which also exists in other recommendation system problems. In the real world, the data distribution of the recommendation system is highly skewed to the popular products, especially on the large-scale dataset with millions of users and items. One more challenge is the class imbalance problem, inherited by the sequential recommendation problem. Generally, each sample only has one positive and thousands of negative samples. To address the above problems together, an innovative Decoupled Representation via Extraction Attention Module (DREAM) is proposed for CDSR to simultaneously learn single- and cross-domain user preference via decoupled representations. A novel Supervised Contrastive Learning framework is introduced to model the inter-sequence relationship as well as address the data sparsity via data augmentations. DREAM also leverages Focal Loss to put more weight on misclassified samples to address the class-imbalance problem, with another uplift on the overall model performance. Extensive experiments had been conducted on two cross-domain recommendation datasets, demonstrating DREAM outperforms various SOTA cross-domain recommendation algorithms achieving up to a 75% uplift in Movie-Book Scenarios.
Overcoming Recommendation Limitations with Neuro-Symbolic Integration

Despite being studied for over twenty years, Recommender Systems (RSs) still suffer from important issues that limit their applicability in real-world scenarios. Data sparsity, cold start, and explainability are some of the most impacting problems. Intuitively, these historical limitations can be mitigated by injecting prior knowledge into recommendation models. Neuro-Symbolic (NeSy) approaches are suitable candidates for achieving this goal. Specifically, they aim to integrate learning (e.g., neural networks) with symbolic reasoning (e.g., logical reasoning). Generally, the integration lets a neural model interact with a logical knowledge base, enabling reasoning capabilities. In particular, NeSy approaches have been shown to deal well with poor training data, and their symbolic component could enhance model transparency. This gives insights that NeSy systems could potentially mitigate the aforementioned RSs limitations. However, the application of such systems to RSs is still in its early stages, and most of the proposed architectures do not really exploit the advantages of a NeSy approach. To this end, we conducted preliminary experiments with a Logic Tensor Network (LTN), a novel NeSy framework. We used the LTN to train a vanilla Matrix Factorization model using a First-Order Logic knowledge base as an objective. In particular, we encoded facts to enable the regularization of the latent factors using content information, obtaining promising results. In this paper, we review existing NeSy recommenders, argue about their limitations, show our preliminary results with the LTN, and propose interesting future works in this novel research area. In particular, we show how the LTN can be intuitively used to regularize models, perform cross-domain recommendation, ensemble learning, and explainable recommendation, reduce popularity bias, and easily define the loss function of a model.
BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation

A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class collaborative filtering (HOCCF) and multi-task learning (MTL) methods has been well studied, there is still a lack of targeted manner in their combined fields, i.e., Multi-behavior Multi-task Recommendation (MMR). To fill the gap, we propose a novel recommendation framework called Behavior-aware Variational AutoEncoder (BVAE), which meliorates the parameter sharing and loss minimization method with the VAE structure to address the MMR problem. Specifically, our BVAE includes behavior-aware semi-encoders and decoders, and a target feature fusion network with a global feature filtering network, while using standard deviation to weigh loss. These modules generate the behavior-aware recommended item list via constructing better semantic feature vectors for users, i.e., from dual perspectives of behavioral preference and global interaction. In addition, we optimize our BVAE in terms of adaptability and robustness, i.e., it is concise and flexible to consume any amount of behaviors with different distributions. Extensive empirical studies on two real and widely used datasets confirm the validity of our design and show that our BVAE can outperform the state-of-the-art related baseline methods under multiple evaluation metrics. The processed datasets, source code, and scripts necessary to reproduce the results can be available at https://github.com/WitnessForest/BVAE.
Analysis Operations for Constraint-based Recommender Systems

Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.
Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation

Although knowledge graph has shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contextual features, i.e., feature sparsity and interaction sparsity. To bridge this gap, in this paper, we propose a novel pairwise intent graph embedding learning (PING) framework to efficiently integrate knowledge graphs into CARS. Specifically, our PING contains three modules: 1) a graph construction module is used to obtain a pairwise intent graph (PIG) containing nodes for users, items, entities, and enhanced intent, where enhanced intent nodes are generated by applying user intent fusion (UIF) on relational intent and contextual intent, and two sub-intents are derived from the semantic information and contextual information, respectively; 2) a pairwise intent joint graph convolution module is used to obtain the refined embeddings of all the features by executing a customized convolution strategy on PIG, where each enhanced intent node acts as a hub to efficiently propagate information among different features and between all the features and knowledge graph; 3) a recommendation module with the refined embeddings is used to replace the randomly initialized embeddings of downstream recommendation models to improve model performance. Finally, we conduct extensive experiments on three public datasets to verify the effectiveness and compatibility of our PING.
Authoring tool for Data Journalists integrating Self-Explanatory Visualization Onboarding Concept for a Treemap Visualization

Recipients of data stories need a certain visualization literacy level to understand and interpret more elaborate data visualizations correctly. For general audiences, however, visualization literacy levels are known to be relatively low. Therefore, journalists face the challenge that they are either restricted to using very simplistic representation techniques that are widely known or have to design and provide additional means to support readers in learning how to read and interpret a particular data visualization. In general, visualization onboarding methods might be utilized to support users in their individual information needs. But with the technology available to date, the effort needed to integrate such features is high and requires programming skills. To address this challenge, we conducted a design study to iteratively develop a proof-of-concept prototype that allows the semi-automatic creation of onboarding instructions for data-journalistic use cases. The implementation is based on a self-developed onboarding library integrated into the widely-used visualization framework called Flourish. To validate our prototype, user experience evaluations were conducted with seven (data) journalists. Our findings indicated that the journalists would provide simple onboarding instructions with fewer onboarding stages and simplified tooltip-based onboarding messages. The prototype can be accessed at http://bit.ly/3gBqrxv.
Real quantum operations and state transformations

Abstract Resource theory of imaginarity provides a useful framework to understand the role of complex numbers, which are essential in the formulation of quantum mechanics, in a mathematically rigorous way. In the first part of this article, we study the properties of ‘real’ (quantum) operations both in single-party and bipartite settings. As a consequence, we provide necessary and sufficient conditions for state transformations under real operations and show the existence of ‘real entanglement’ monotones. In the second part of this article, we focus on the problem of single copy state transformation via real quantum operations. When starting from pure initial states, we completely solve this problem by finding an analytical expression for the optimal fidelity of transformation, for a given probability of transformation and vice versa. Moreover, for state transformations involving arbitrary initial states and pure final states, we provide a semidefinite program to compute the optimal achievable fidelity, for a given probability of transformation.
Trending Now: Modeling Trend Recommendations

Modern recommender systems usually include separate recommendation carousels such as 'trending now' to list trending items and further boost their popularity, thereby attracting active users. Though widely useful, such 'trending now' carousels typically generate item lists based on simple heuristics, e.g., the number of interactions within a time interval, and therefore still leave much room for improvement. This paper aims to systematically study this under-explored but important problem from the new perspective of time series forecasting. We first provide a set of rigorous definitions related to item trendiness and formulate the trend recommendation task as a one-step time series forecasting problem. We then propose a deep latent variable model, dubbed Trend Recommender (TrendRec), to forecast items' future trends and generate trending item lists. Furthermore, we design associated evaluation protocols for trend recommendation. Experiments on real-world datasets from various domains show that our TrendRec significantly outperforms the baselines, verifying our model's effectiveness.
Integrating Item Relevance in Training Loss for Sequential Recommender Systems

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.
Contextualized Experiential Language Learning in the Metaverse

Metaverse is an immersive environment that may support in an effective way contextualized experiential language learning, offering the possibility of collaboration, inclusion, and social interaction among students in context simulating real situations. In this paper, we propose MetaCLA, an immersive multi-user virtual environment based on oculus Meta Quest 2 that offers contextualized experiences for interactive language lab activities conducted in a small group. The environment is equipped with interactive objects useful for learning vocabulary, stimulating discussions, and role-playing. We also conduct a preliminary evaluation involving an English classroom composed of 15 students and a teacher. We assess the presence and easiness to use perceptions, the easiness of use, and the net promoter score of participants. First results were encouraging.
Orbital expansion variational quantum eigensolver

Abstract Variational quantum eigensolver (VQE) has emerged as a promising method for investigating ground state properties in quantum chemistry, materials science, and condensed matter physics. However, the conventional VQE method generally lacks systematic improvement and convergence guarantees, particularly when dealing with strongly correlated systems. In light of these challenges, we present a novel framework called orbital expansion VQE (OE-VQE) to address these limitations. The key idea is to devise an efficient convergence path by utilizing shallower quantum circuits, starting from a highly compact active space and gradually expanding it until convergence to the ground state is achieved. To validate the effectiveness of the OE-VQE framework, we conducted benchmark simulations on several small yet representative molecules, including the <?CDATA $\mathrm{H}_{6}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>6</mml:mn> </mml:mrow> </mml:msub> </mml:math> chain, <?CDATA $\mathrm{H}_{10}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">H</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> </mml:msub> </mml:math> ring and <?CDATA $\mathrm{N}_2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:math> . The simulation results demonstrate that our proposed convergence paths significantly enhance the performance of conventional VQE. Overall, our work sheds valuable insight into the simulation of molecules based on shallow quantum circuits, offering a promising avenue for advancing the efficiency and accuracy of VQE approaches in tackling complex molecular systems.
RNN and LSTM Models for Arabic Speech Commands Recognition Using PyTorch and GPU

Because of the acute lack of Arabic-language datasets, speech command recognition, particularly in Arabic, is a real challenge for research. Contrary to the English language, the Google Speech Commands dataset is the most well-known dataset for spoken instructions. This has accelerated study and sparked many new deep techniques that address keyword research challenges. Two classes' classification are introduced in this paper ) with LSTM & RNN. Moreover, general-purpose computing on the graphics processing unit will be applied to train the model, reducing the training time. To ensure that the parameters used in our model are the best, several training experiments were performed on different parameters to check the system's efficiency. The results demonstrate that the proposed technique has satisfactory accuracy. The training time was extremely short due to the technology used (CUDA Toolkit), and, last but not least, the model recognized the commands.
A Novel Model for Optimizing Multilayer Perceptron Neural Network Architecture Based on Genetic Algorithm Method

Multilayer perceptron (MLP) have been widely used in a variety of applications and fields. The hyper-parameters of such a machine learning (ML) models must be tuned to fit it into different problems. Choosing the best hyper-parameter configuration has a direct impact on the model performance. This work proposes a new optimization model, in order to find the optimal neural architecture solved by genetic algorithm method. We use a real architecture-representing chromosome that can express both the number of layers and the number of nodes in each layer. This novel proposed approach models the challenge of neural architecture optimization as non-linear constraint programming with mixed variables. The generalization potential of the MLP was further assessed, and the risk of over fitting was avoided, using a fold cross-validation technique. Results from the Iris dataset show an improvement in classification performance over earlier studies. The stability of the technique is also shown by the fact that the proposed method has the minimum of the mean accuracy rate’s standard deviation.