Owing to their widespread practicability, recommendation systems play an important role in our daily life. Recently, several studies have examined recommendations based on learning from user purchase history using association rules to extract the complementary and substitution relationships between items. However, the integration of generalized rules into a customized recommendation model is a challenging task. This study proposes a novel recommendation model, A3BRec, which incorporates association rules with a transformer network to predict potentially interesting items for the next basket. We introduce a ternary-stage framework to integrate basket associations into sequential next-basket recommendations. Furthermore, extensive experiments were conducted on real-world datasets to demonstrate the performance and superiority of the proposed model over the state-of-the-art methods for various evaluation metrics. We also use a case study to show the improvement and influence of the proposed ternary integration in A3BRec on recommendation quality.

Outfit coordination is a direct way for people to express themselves. However, judging the compatibility between tops and bottoms requires considering multiple factors such as color and style. This process is time-consuming and prone to errors. In recent years, the development of large language models and large multi-modal models has transformed many application fields. This study aims to explore how to leverage these models to achieve breakthroughs in fashion outfit recommendations. This research combines the keyword response text from the large language model Gemini in the Vision Question Answering (VQA) task with the deep feature fusion technology of the large multi-modal model Beit3. By providing only image data of the clothing, users can evaluate the compatibility of tops and bottoms, making the process more convenient. Our proposed model, the Large Multi-modality Language Model for Outfit Recommendation (LMLMO), outperforms previously proposed models on the FashionVC and Evaluation3 datasets. Moreover, experimental results show that different types of keyword responses have varying impacts on the model, offering new directions and insights for future research.

Recently, due to the surge in e-commerce, growing attention has been paid to how to recommend a customer's next purchase based on sequential or session-based data. However, most prior studies have generally focused on what items may be interesting for users, but have neglected the consideration of when the next items are likely to be purchased. Clearly, the timing information is an essential factor for companies to adopt proper selling strategies at the “right” time. In this study, a novel recommendation system, G-TransRec, is proposed to predict customers’ next items of interest with the potential purchase time by exploiting a user temporal interaction sequence. Moreover, by integrating the graph embedding technique, we include the global user information to explore more collaborative knowledge for effective recommendations. Several experiments were conducted on two real datasets to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on several evaluation metrics. We also use a case study to show the practicability of the proposed G-TransRec for users to recommend what they want at what time from a massive amount of merchandise.

Collaborative filtering (CF) technology has been widely used in recommendation systems. Usually, the latent factor model (LFM) is used as the basis for implementing CF recommendation in deep learning systems. This study differs from previous studies in two respects. First, for different target items, the user embedding vector should be dynamically adjusted according to the content of the target item. Therefore, we have added an attention mechanism to dynamically adjust the user’s embedding vector. However, people’s preferences usually change over time. Therefore, based on the above attention model, this study considers two time-decay functions to emphasize the user’s recent preferences. The first decay function considers the situation where the recent rating is more important than the long ago rating. The second time-decay function considers the situation, whereby users generally prefer movies that have been released recently rather than movies that have been released a long time ago. By combining these two time-decay functions with the attention model, we propose a time-decay adaptive latent factor model (TDADLFM) model for item score prediction. This study applies this model to a dataset integrating Movielens-10M and HetRec2011 and proves that all three new considerations can improve recommendation performance.

Abstract Attribute‐oriented induction (AOI) is a data analysis technique based on induction. The traditional AOI algorithm requires a threshold given by users to determine the number of output tuples. However, it is not easy to set an appropriate tuple threshold, and there is usually noise contained in a dataset. The traditional AOI algorithm can only generate a summary output of a fixed size, but it cannot guarantee that all generalized tuples have sufficient specificity and representativeness. In this article, a new AOI method is proposed to make up for the shortcomings. We introduce the concept of cost to measure the loss of accuracy due to attribute ascension. We also propose two algorithms based on the hierarchical clustering method. By setting cost constraints on each generalized tuple, our method can generate accurate output while eliminating noise, and help users get more informative and clearer results.

The decision feedback equalizer (DFE) is an efficient scheme to suppress intersymbol interference (ISI) in various communication and magnetic recording systems. However, most DFE implementations suffer from the phenomenon of error propagation, which degrades its bit error rate (BER) performance. In this paper, We use sphere detector (SD) to achieve maximum likelihood (ML) detection and significantly reduce the system symbol error rate (SER). Simulations show that the proposed scheme with sphere detector decision feedback equalizer (SD-DFE) algorithm can efficiently reduce the SER. At SNR=28, the SER can be improved from 2.0 × 10−5(Ideal DFE) to 1.8 × 10−6(six-stage SD-DFE).

Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total‐Community‐Purity and Total‐Cluster‐Purity both at approximately 0.8.

Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text‐mining‐based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively.

Structured continuous-label classification is a variety of classification in which the label is continuous in the data, but the goal is to classify data into classes that are a set of predefined ranges and can be organized in a hierarchy. In the hierarchy, the ranges at the lower levels are more specific and inherently more difficult to predict, whereas the ranges at the upper levels are less specific and inherently easier to predict. Therefore, both prediction specificity and prediction accuracy must be considered when building a decision tree (DT) from this kind of data. This paper proposes a novel classification algorithm for learning DT classifiers from data with structured continuous labels. This approach considers the distribution of labels throughout the hierarchical structure during the construction of trees without requiring discretization in the preprocessing stage. We compared the results of the proposed method with those of the C4.5 algorithm using eight real data sets. The empirical results indicate that the proposed method outperforms the C4.5 algorithm with regard to prediction accuracy, prediction specificity, and computational complexity.

Singular value decomposition (SVD) is an optimal method to obtain spatial multiplexing gain in multi-input multi-output (MIMO) channels. However, the high cost of implementation and high decomposing latency of the SVD restricts its usage in current wireless communication applications. In this paper, we present a complete adaptive SVD algorithm and a reconfigurable architecture for high-throughput MIMO-orthogonal frequency division multiplexing systems. There are several proposed architectural design techniques: reconfigurable scheme, division-free adaptive step size scheme, early termination scheme, and data interleaving scheme. The reconfigurable scheme can support all antenna configurations in a MIMO system. The division-free adaptive step size and early termination schemes are used to effectively reduce the decomposing latency and improve hardware utilization. The data interleaving scheme helps to deal with several channel matrices concurrently. Besides, we propose an orthogonal reconstruction scheme to obtain more accurate SVD outputs, and then the system performance will be greatly enhanced. We apply our SVD design to the IEEE 802.11 n applications. This design is implemented and fabricated in UMC 90 nm 1P9M CMOS technology. The maximum operating frequency is measured to be at 101.2 MHz, and the corresponding power dissipation is at 125 mW. The core size is 2.17 mm2and the die size occupies 4.93 mm2. The chip result shows that the average latency is only 0.33% of the wireless local area network coherence time. Hence, the proposed reconfigurable adaptive SVD engine design is very suitable for high-throughput wireless communication applications.

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.

In this paper, new echo and NEXT cancellers are proposed for echo and NEXT cancellation in full-duplex digital transmission over 10GBASE-T system. The proposed cancellation schemes inherit the concept of the adaptive interpolated FIR (AIFIR)-based crosstalk canceller, where the long portion is modeled by an adaptive sparse FIR filter. Furthermore, we also employ the channel shortening technique to shorten the impulse response of crosstalk interference. Hence, the complexity of echo and NEXT cancellers can be greatly reduced. Simulations show that compared with the conventional architecture, although the performance is degraded by 1.5 dB, it still meets the SNR requirement in 10GBASE-T system. The complexity reduction of the proposed echo and NEXT cancellation schemes in arithmetic is about 70% and 65% respectively. The saving of hardware complexity results in computationally efficient VLSI implementation of the echo and NEXT cancellers in 10GBASE-T system.

Petri nets have long been used in modeling and simulating project execution because of their great capability to describe concurrent activities and simulate the evolvement of processes. Although a number of extended Petri net models have been proposed to model and simulate resource sharing and activity dependence in projects, none of them has ever included a resource assignment mechanism into their models. Because resource assignments influence how limited resources are allocated among conflicting activities, they may heavily affect the availability of resources and the execution of projects. Therefore, a model without considering resource-sharing and resource assignment strategies may lead to a misunderstanding about project scheduling, resource consumption behaviors, and estimated project time. Accordingly, this paper proposes a new extended Petri net model that can describe how resources are shared and assigned among concurrent activities of multiple projects. The proposed model is named as resource assignment Petri net (RAPN), which extends an object composition Petri net with new places, transitions, attributes, and firing rules to model resource-sharing and resource assignment strategies. Finally, we prove that RAPN can correctly model the resource consumption behaviors of projects and can correctly compute the total elapsed time of projects.

Previous research on mining sequential patterns mainly focused on discovering patterns from point-based event data. Little effort has been put toward mining patterns from interval-based event data, where a pair of time values is associated with each event. Kam and Fu's work [31] in 2000 identified 13 temporal relationships between two intervals. According to these temporal relationships, a new variant of temporal patterns was defined for interval-based event data. Unfortunately, the patterns defined in this manner are ambiguous, which means that the temporal relationships among events cannot be correctly represented in temporal patterns. To resolve this problem, we first define a new kind of nonambiguous temporal pattern for interval-based event data. Then, the TPrefixSpan algorithm is developed to mine the new temporal patterns from interval-based events. The completeness and accuracy of the results are also proven. The experimental results show that the efficiency and scalability of the TPrefixSpan algorithm are satisfactory. Furthermore, to show the applicability and effectiveness of temporal pattern mining, we execute experiments to discover temporal patterns from historical Nasdaq data.

Data mining extracts implicit, previously unknown, and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding association rules. Although a large amount of research has been devoted to this subject, none of it finds association rules from directed acyclic graph (DAG) data. Without such a mining method, the hidden knowledge, if any, cannot be discovered from the databases storing DAG data such as family genealogy profiles, product structures, XML documents, task precedence relations, and course structures. In this article, we define a new kind of association rule in DAG databases called the predecessor–successor rule, where a node x is a predecessor of another node y if we can find a path in DAG where x appears before y. The predecessor–successor rules enable us to observe how the characteristics of the predecessors influence the successors. An approach containing four stages is proposed to discover the predecessor–successor rules. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 621–637, 2006.

The problem addressed in This work is to discover the frequently occurred sequential patterns from databases. Although much work has been devoted to this subject, to the best of our knowledge, no previous research was able to find sequential patterns from d-dimensional sequence data, where d>2. Without such a capability, many practical data would be impossible to mine. For example, an online stock-trading site may have a customer database, where each customer may visit a Web site in a series of days; each day takes a series of sessions and each session visits a series of Web pages. Then, the data for each customer forms a 3-dimensional list, where the first dimension is days, the second is sessions, and the third is visited pages. To mine sequential patterns from this kind of sequence data, two efficient algorithms have been developed in This work.

Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items. An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.

A multimedia presentation model provides designers a tool to formally specify the temporal and spatial relationships of objects. The formality helps designers to communicate with others, to check the integrity of designs, and provides a chance to simulate the designs. Although much research has been devoted to this subject, to the best of our knowledge, no multimedia models are able to describe the spatial-temporal relations of moving objects that may refer to each other for computing displaying addresses. The addresses may be recomputed several times during the objects' lifetimes to reflect their movements. Without a formal model, designers are forced to specify the relationships in an ad hoc manner that causes misunderstanding and hampers integrity check. The check includes if an object gets its addresses in time from another object, if an object is displayed in the right places, etc. The difficulty of designing such a formal model lies in integrating temporal constraints of objects with a real-time address transferring mechanism. In this paper, we present an extended Petri-net model, which models concurrent relationships of objects with new places, transitions, and firing rules to transfer and transform addresses in real time. Its descriptive power and correctness is demonstrated by five patterns of multimedia presentations and a sample play scripts.