Collaborative filtering is one of the most fundamental topics for recommender systems. Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods. Being inspired by recent successes of graph filtering-based methods and score-based generative models (SGMs), we present a novel concept of blurring-sharpening process model (BSPM). SGMs and BSPMs share the same processing philosophy that new information can be discovered (e.g., new images are generated in the case of SGMs) while original information is first perturbed and then recovered to its original form. However, SGMs and our BSPMs deal with different types of information, and their optimal perturbation and recovery processes have fundamental discrepancies. Therefore, our BSPMs have different forms from SGMs. In addition, our concept not only theoretically subsumes many existing collaborative filtering models but also outperforms them in terms of Recall and NDCG in the three benchmark datasets, Gowalla, Yelp2018, and Amazon-book. In addition, the processing time of our method is comparable to other fast baselines. Our proposed concept has much potential in the future to be enhanced by designing better blurring (i.e., perturbation) and sharpening (i.e., recovery) processes than what we use in this paper. Our code is available at https://github.com/jeongwhanchoi/BSPM.

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.

Considering the trend of the vehicle market where the vehicle becomes quieter, in-vehicle rattling noise is significant criterion for the quality of the vehicle. Though the latest deep learning algorithms have been introduced for classifying in-vehicle rattling noise, there are limitations due to impulsive and transient nature of rattling noise and reflective and refractive characteristics of in-vehicle environment. In this paper, we propose a novel beamforming method that extracts intra-interchannel spatial features by parameterizing the optimal beamforming weights including Direction-of-Arrival (DOA) function to overcome the addressed problem. The proposed method outperformed the existing deep learning algorithms with 0.9270 accuracy and verified by 10-fold cross validation and chi-squared test. In addition, it is shown that the time cost for classification of rattling noise is appropriate for real-time classification as a side-effect of using convolution-pooling operations.

Point-of-interest (POI) recommendation can help providing better user experience, and provide users with third-party information about restaurant or entertainment. There are several studies to predict the next POI where the user will go so as to recommend appropriate services. They use additional information such as text or location for more precise prediction, or manually define user patterns. However, it is costly to collect and analyze large amounts of data for POI recommendation. In this paper, we propose a novel method to recommend POI by extracting the personalized movement pattern only from the POI data without any additional information. We collected POI data ofl. 5M users for six months from smart card, and produce personalized POI and user embedding. Since it is hard to construct one POI recommendation model for 1.5 million people, we divide them to several groups according to their simple mobility pattern. Given a previous POI sequence, user and group id, the proposed model is trained to maximize the probability of the next POI. Although the learning method of the proposed model is simple, even if the given POI sequence is the same, successive POI can be predicted differently according to the user, resulting in personalized POI recommendation. The proposed model achieves 73.64%, 88.65%, and 91.54% in top-1, 3 and 5 accuracies which are higher than the performance of the baseline model (59.48%, 75.85%, and 80.1%, respectively). Besides, we verify the embedding performance of the proposed model through arithmetic operations between POI vectors.

SGML/XML are having a profound impact on data modeling and processing. We present an efficient algorithm to compute differences between old and new versions of an SGML/XML document. The difference between the two versions can be considered to be an edit script that transforms one document tree into another. The proposed algorithm is based on a hybridization of bottom-up and top-down methods: The matching relationships between nodes in the two versions are produced in a bottom-up manner and then the top-down breadth-first search computes an edit script. Faster matching is achieved because the algorithm does not need to investigate the possible existence of matchings for all nodes. Furthermore, it can detect structurally meaningful changes such as the movement and copy of a subtree as well as simple changes to the node itself like insertion, deletion, and update.

This paper presents a syntactic method for sophisticated logical structure analysis that transforms document images with multiple pages and hierarchical structure into an electronic document based on SGML/XML. To produce a logical structure more accurately and quickly than previous works of which the basic units are text lines, the proposed parsing method takes text regions with hierarchical structure as input. Furthermore, we define a document model that is able to describe geometric characteristics and logical structure information of documents efficiently and present its automated creation method. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) show that the method has performed logical structure analysis successfully and generated a document model automatically. Particularly, the method generates SGML/XML documents as the result of structural analysis, so that it enhances the reusability of documents and independence of platform.

The evolutionary approach to artificial neural networks has been rapidly developing in recent years and shows great potential as a powerful tool. However, most evolutionary neural networks have paid little attention to the fact that they can evolve from modules. This paper presents a hybrid method of modular neural networks and evolutionary algorithm as a promising model for intelligent systems. To build a neural network system that is rich in autonomy and creativity, some ideas of artificial life have been adopted. This paper describes the concepts and methodologies for the evolvable model of modular neural networks, which might not only develop spontaneously new functionality, but also grow and evolve its own structure autonomously. We show the potential of the method by applying it to a visual categorization task with handwritten digits. The evolutionary mechanism has shown a strong potential to generate useful network architectures from an initial set of randomly connected networks. © 1998 John Wiley & Sons, Inc.