Cross-domain sequential recommendation (CDSR) tackles data sparsity and cold-start issues by leveraging information from the source domain to enhance prediction accuracy in the target domain. However, the recommendation fairness issue may further deteriorate dramatically with the biased knowledge transfer of overlapped users. This paper is the first study to address and improve fairness measurement between different demographic groups in CDSR. The proposed FairCDSR employs sequence augmentation techniques to enrich the interaction histories of disadvantaged user groups, which typically have less training data. These augmented sequences are further represented by a contrastive learning method with hard negative sampling to mitigate the unfairness in recommendations. Then, to more precisely capture cross-domain preferences, a multi-interest learning approach is applied to each group across the domains. Finally, an interest-level knowledge transfer algorithm with fixed bandwidth limitations for each group is developed to extract fair and semantic cross-domain information. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of FairCDSR. Compared to existing cross-domain or fair recommendation systems, FairCDSR significantly reduces recommendation disparity between advantaged and disadvantaged groups. Benefiting from a significant improvement in the recommendation accuracy of the disadvantaged group, the overall system performance can also be effectively enhanced by 5-10%.

Abstract Artificial intelligence techniques for financial time series analysis have been used to enhance momentum trading methods. However, most previous studies, which have treated stocks as independent entities, have overlooked the significance of correlations among individual stocks, thus compromising portfolio performance. To address this gap, a momentum trading framework is proposed that combines heterogeneous data, such as corporate governance factors and financial domain knowledge, to model the relationships between stocks. Our approach involves adopting a knowledge graph embedding approach to map relations among heterogeneous relationships in the data, which is then utilized to train a multitask supervised learning approach based on a learning-to-rank algorithm. This method culminates in a robust portfolio selection method on the basis of the framework. Experimental results using data from the Taiwan Stock Exchange demonstrate that our proposed method outperforms traditional linear models and other machine learning methods in predictive ability. The investment portfolio constructed serves as an invaluable aid to investment decision-making.

People consume items not only by themselves but also in groups that include their family, friends, coworkers, and online social groups. Different from individual recommendation systems, group recommendation systems first consider group member’s preference separately. The impacts among members are considered, and then, the final group preference and decision would be generated. However, existing group recommendation models suffer severer data sparsity problems than traditional recommendation systems. There is currently lack of a systematic approach to properly address the above issue. What is worse, previous works less consider situations that users may have diverse interests, which means that users may change their preferences by considering the preferences of other group members. Here, we propose a member-augmented multi-interest model with knowledge graph (KG) embeddings to overcome the aforementioned drawbacks. Because only positive labels of groups can be identified in a dataset, precisely predicting a group’s opinion on items that members have not been exposed to is difficult. Accordingly, our model applies the member augmentation (MA) technique to precisely predict a group’s opinion on items. In addition, we leverage multi-interest framework to model the change of diverse user preferences. The framework can know which interests of the user will affect the decision of the group, and interests will vary with different group members. Experiments indicated that the proposed model improves the performance by around 20%, 10%, and 2% in MaFengWo, Yelp, and Meetup-NYC, respectively.

The significant increase in credit card transactions can be attributed to the rapid growth of online shopping and digital payments, particularly during the COVID-19 pandemic. To safeguard cardholders, e-commerce companies, and financial institutions, the implementation of an effective and real-time fraud detection method using modern artificial intelligence techniques is imperative. However, the development of machine-learning-based approaches for fraud detection faces challenges such as inadequate transaction representation, noise labels, and data imbalance. Additionally, practical considerations like dynamic thresholds, concept drift, and verification latency need to be appropriately addressed. In this study, we designed a fraud detection method that accurately extracts a series of spatial and temporal representative features to precisely describe credit card transactions. Furthermore, several auxiliary self-supervised objectives were developed to model cardholders’ behavior sequences. By employing intelligent sampling strategies, potential noise labels were eliminated, thereby reducing the level of data imbalance. The developed method encompasses various innovative functions that cater to practical usage requirements. We applied this method to two real-world datasets, and the results indicated a higher F1 score compared to the most commonly used online fraud detection methods.

The growing number of publications in the field of artificial intelligence highlights the need for researchers to enhance their efficiency in searching for relevant articles. Most paper recommendation models either rely on simplistic citation relationships among papers or focus on content-based approaches, both of which overlook interactions within academic networks. To address the aforementioned problem, knowledge graph embedding (KGE) methods have been used for citation recommendations because recent research proves that graph representations can effectively improve recommendation model accuracy. However, academic networks are dynamic, leading to changes in the representations of users and items over time. The majority of KGE-based citation recommendations are primarily designed for static graphs, thus failing to capture the evolution of dynamic knowledge graph (DKG) structures. To address these challenges, we introduced the evolving knowledge graph embedding (EKGE) method. In this methodology, evolving knowledge graphs are input into time-series models to learn the patterns of structural evolution. The model has the capability to generate embeddings for each entity at various time points, thereby overcoming limitation of static models that require retraining to acquire embeddings at each specific time point. To enhance the efficiency of feature extraction, we employed a multiple attention strategy. This helped the model find recommendation lists that are closely related to a user’s needs, leading to improved recommendation accuracy. Various experiments conducted on a citation recommendation dataset revealed that the EKGE model exhibits a 1.13% increase in prediction accuracy compared to other KGE methods. Moreover, the model’s accuracy can be further increased by an additional 0.84% through the incorporation of an attention mechanism.

Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user–item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate cold start and data sparsity issues. Although the graph-based CDRs can capture valuable information, they still have some limitations. First, embeddings are highly vulnerable to noisy interactions, because the message aggregation in the graph convolutional network can further enlarge the impact. Second, because of the property of graph-structured data, the influence of high-degree nodes on representation learning is more than that of the long-tail items, and this can cause a poor recommendation performance. In this study, we devised a novel A daptive A dversarial C ontrastive L earning framework for graph-based C ross- D omain R ecommendation ( ACLCDR ). The ACLCDR introduces reinforcement learning to generate adaptive augmented samples for contrastive learning tasks. Then, we leveraged a multitask training strategy to jointly optimize the model with auxiliary tasks. Finally, we verified the effectiveness of the ACLCDR through nine real-world cross-domain tasks adopted from Amazon and Douban. We observed that ACLCDR exceeded the best state-of-the-art baseline by 25%, 42.5%, 16.3%, and 23.8% in terms of HR@ 10 and NDCG@10 for the Music & Movie task from Amazon.

The data of recommendation systems typically only contain the purchased item as positive data and other un-purchased items as unlabeled data. To train a good recommendation model, in addition to the known positive information, we also need high-quality negative information. Capturing negative signals in positive and unlabeled data is challenging for recommendation systems. Most studies have used specific data and proposed negative sampling methods suitable to the data characteristics. Existing negative sampling strategies cannot automatically select suitable approaches for different data. However, this one-size-fits-all strategy often makes potential positive samples considered as negative, or truly negative samples considered as potential positive samples and recommend to users. In this way, it will not only turn down the recommendation result, but even also have an adverse effect. Accordingly, we propose a novel negative sampling model, Reinforced PU-learning with Hybrid Negative Sampling Strategies for Recommendation (RHNSR), which can combine multiple sampling strategies and dynamically adjust the proportions used by different sampling strategies. In addition, ensemble learning, which integrates various model sampling strategies for obtaining an improved solution, was applied to RHNSR. Extensive experiments were conducted on three real-world recommendation datasets, and the experimental results indicated that the proposed model significantly outperformed state-of-the-art baseline models and revealed significant improvements in precision and hit ratio (49.02% and 37.41%, respectively).

Next-item recommendation involves predicting the next item of interest of a given user from their past behavior. Users tend to browse and purchase various items on e-commerce websites according to their varied interests and needs, as reflected in their purchasing history. Most existing next-item recommendation methods aim at extracting the main point of interest in each browsing session and encapsulate it in a single representation. However, past behavior sequences reflect the multiple interests of a single user, which cannot be captured by methods that focus on single-interest contexts. Indeed, multiple interests cannot be captured in a single representation, and doing so results in missing information. Therefore, we propose a model with a multi-interest structure for capturing the various interests of users from their behavior sequence. Moreover, we adopted a method based on a graph neural network to construct interest graphs based on the historical and current behavior sequences of users. These graphs can capture complex item transition patterns related to different interests. In experiments, the proposed method outperforms state-of-the-art session-based recommendation systems on three real-world datasets, achieving 4% improvement of Recall over the SOTAs on Jdata dataset.

User interaction history with items is used to infer user preferences in conventional recommendation systems. Among these, conversational recommendation systems (CRSs), which provide effective recommendations based on a framework combining recommendations and conversations with users, have been proposed. However, existing CRS model still have some shortcomings, such as the lack of using dialogue records for recommendations. In this research, a method was proposed to solve multiround conversational recommendations with heterogeneous questions. The model included a hierarchical reinforcement learning framework and introduced methods of effectively incorporating user feedback for online recommender updates. Experiments were conducted on several datasets to verify model effectiveness; the model outperformed the current state-of-the-art method. Finally, the architecture was more realistic than other conversational recommendation scenarios and provided richer explanations.

In this paper, an optimization approach is proposed to estimate the 3D indoor Manhattan scene layout from a single input image. The proposed system models the interior space as a three-dimensional box which includes ceiling, floor, and walls. The regions corresponding to different surfaces can be calculated by projecting the 3D box onto the two-dimensional image with suitable camera and box parameters. This paper also utilizes the consistency of coplanar lines and the boundary edges between different surfaces to design a cost function. The rotation, translation, and box parameters of the interior layout can be estimated with an energy minimization process. In the experimental results, we apply the proposed algorithm to a number of real images of interior scenes to demonstrate the effectiveness of the proposed system.

Behavioral finance is a relatively new and developing research field which adopts cognitive psychology and emotional bias to explain the inefficient market phenomenon and some irrational trading decisions. Unlike the experts in this field who tried to reason the price anomaly and applied empirical evidence in many different financial markets, we employ the advanced binary classification algorithms, such as AdaBoost and support vector machines, to precisely model the overreaction and strengthen the portfolio compositions of the contrarian trading strategies. The novelty of this article is to discover the financial time-series patterns through a high-dimensional and nonlinear model which is constructed by integrated knowledge of finance and machine learning techniques. We propose a dual-classifier learning framework to select candidate stocks from the past results of original contrarian trading strategies based on the defined learning targets. Three different feature extraction methods, including wavelet transformation, historical return distribution, and various technical indicators, are employed to represent these learning samples in a 381-dimensional financial time-series feature space. Finally, we construct the classifier models with four different learning kernels and prove that the proposed methods could improve the returns dramatically, such as the 3-year return that improved from 26.79% to 53.75%. The experiments also demonstrate significantly higher portfolio selection accuracy, improved from 57.47% to 66.41%, than the original contrarian trading strategy. To sum up, all these experiments show that the proposed method could be extended to an effective trading system in the historical stock prices of the leading U.S. companies of S&P 100 index.

Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

This paper presents an interactive video event retrieval system based on improved adaboost learning. This system consists of three main steps. Firstly, a long video sequence is partitioned into several video clips by using a distribution-based approach instead of detecting shot transition boundaries. Secondly, audiovisual features (i.e., color, motion and audio features) are extracted from video sequences for video clip representation. Finally, the modified AdaBoost learning algorithm is employed for interactive video retrieval with relevance feedback. This AdaBoost learning algorithm differs from conventional AdaBoost learning methods mainly in the selection of paired video features for the weak classifiers. Experimental results show improved performance of video retrieval by using the proposed system

In this paper, we propose a novel and fast face detection algorithm for detecting face in color video sequences. This algorithm can be integrated into a real-time surveillance or a video retrieval in the design of the algorithm. A set of multiresolution Haar wavelet coefficients pairs is selected by the proposed learning algorithm to determine if a particular region is a face. We apply an ID3-like balanced decision tree for the wavelet coefficients quantization, to reduce the quantization error. For each pair of quantized features, we estimate the associated conditional joint probability density function from a large set of face and non-face training data. Then, we compute the Kullback Leibler (KL) distance to measure the discrimination between the face and non-face conditional density functions for each feature pair. The feature pairs with larger KL-distance are selected as the feature candidates. It is an effective feature dimension reduction method and helps to speedup the Adaboost training algorithm when considering the spatial relationship between all coefficient pairs. Aided by an automatic skin color judgment method and a Gaussian face location model both in temporal and spatial domain, the experiments show that the proposed algorithm runs faster than 4 times the video rate with good detection accuracy.