| 文献作者 | Huiyuan ChenandJing Li | ||||||||||
| 文献发表年限 | 2020 | 创建时间 | 2020-12-21 | ||||||||
| 文献关键字 | 生成对抗学习；Adversarial；神经网络复杂度分析； | ||||||||||
| 摘要描述 | |||||||||||
| 文献作者 | Zhu Sun; Jie Zhang | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2020-04-15 | ||||||||
| 文献关键字 | category information | ||||||||||
| 摘要描述 | Item category has proven to be useful additional informa- tion to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many real applications, item category is often organized in a richer knowledge structure - category hierar- chy, to reflect the inherent correlations among different cate- gories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifi- cally, a user can be influenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the influence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms. | ||||||||||
| 文献作者 | HUI XIONG | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2019-12-13 | ||||||||
| 文献关键字 | 主题模型；生成模型；解释；利用物品特征和集合关系构造物品之间的联系；构图模式 | ||||||||||
| 摘要描述 | Product bundling is a marketing strategy that offers several products/items for sale as one bundle. While the bundling strategy has been widely used, less efforts have been made to understand how items should be bundled with respect to consumers’ preferences and buying motives for product bundles. This article investigates the relationships between the items that are bought together within a product bundle. To that end, each purchased product bundle is formulated as a bundle graph with items as nodes and the associations between pairs of items in the bundle as edges. The relationships between items can be analyzed by the formation of edges in bundle graphs, which can be attributed to the associations of feature aspects. Then, a probabilistic model BPM (Bundle Purchases with Motives) is proposed to capture the composition of each bundle graph, with two latent factors node-type and edge-type introduced to describe the feature aspects and relationships respectively. Furthermore, based on the preferences inferred from the model, an approach for recommending items to form product bundles is developed by estimating the probability that a consumer would buy an associative item together with the item already bought in the shopping cart. Finally, experimental results on real-world transaction data collected from well-known shopping sites show the effectiveness advantages of the proposed approach over other baseline methods. Moreover, the experiments also show that the proposed model can explain consumers’ buying motives for product bundles in terms of different node-types and edge-types | ||||||||||
| 文献作者 | Fuzhen Zhuang; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-12-12 | ||||||||
| 文献关键字 | attention model; 多任务学习 | ||||||||||
| 摘要描述 | Multi-task learning is a successful machine learning framework which improves the performance of prediction models by leveraging knowledge among tasks, e.g., the relationships between different tasks. Most of existing multi-task learning methods focus on guiding learning process by predefined task relationships. In fact, these methods have not fully exploited the associated relationships during the learning process. On the one hand, replacing predefined task relationships by adaptively learned ones may result in higher prediction accuracy as it can avoid the risk of misguiding caused by improperly predefined relationships. On the other hand, apart from the task relationships, feature-task dependence and feature-feature interactions could also be employed to guide the learning process. Along this line, we propose a Multiple Relational Attention Network (MRAN) framework for multi-task learning, in which three types of relationships are considered. Correspondingly, MRAN consists of three attention-based relationship learning modules: 1) a task-task relationship learning module which captures the relationships among tasks automatically and controls the positive and negative knowledge transfer adaptively; 2) a featurefeature interaction learning module that handles the complicated interactions among features; 3) a task-feature dependence learning module, which can associate the related features with target tasks separately. To evaluate the effectiveness of the proposed MARN, experiments are conducted on two public datasets and a real-world dataset crawled from a review hosting site. Experimental results demonstrate the superiority of our method over both classical and the state-of-the-art multi-task learning methods. | ||||||||||
| 文献作者 | FULI FENG, XIANGNAN HE | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-12-06 | ||||||||
| 文献关键字 | Stock Prediction, Learning to Rank, Graph-based Learning, GCN； 股票收益预测 | ||||||||||
| 摘要描述 | Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized towards the target of investment, i.e., selecting the best stock with highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trend) or a regression problem (to predict stock price). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively. | ||||||||||
| 文献作者 | Thomas N. Kipf; Max Welling | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2019-12-05 | ||||||||
| 文献关键字 | GCN; 图卷积简化过程 | ||||||||||
| 摘要描述 | We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin. | ||||||||||
| 文献作者 | Yanjie Fu; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-12-03 | ||||||||
| 文献关键字 | Mobile User Profiling, Substructure, Representation Learning; 生成对抗网络； | ||||||||||
| 摘要描述 | Mobile user profiles are a summary of characteristics of user-specific mobile activities. Mobile user profiling is to extract a user’s interest and behavioral patterns from mobile behavioral data. While some efforts have been made for mobile user profiling, existing methods can be improved via representation learning with awareness of substructures in users’ behavioral graphs. Specifically, in this paper, we study the problem of mobile users profiling with POI check-in data. To this end, we first construct a graph, where a vertex is a POI category and an edge is the transition frequency of a user between two POI categories, to represent each user. We then formulate mobile user profiling as a task of representation learning from user behavioral graphs. We later develop a deep adversarial substructured learning framework for the task. This framework has two mutually-enhanced components. The first component is to preserve the structure of the entire graph, which is formulated as an encoding-decoding paradigm. In particular, the structure of the entire graph is preserved by minimizing reconstruction loss between an original graph and a reconstructed graph. The second component is to preserve the structure of subgraphs, which is formulated as a substructure detector based adversarial training paradigm. In particular, this paradigm includes a substructure detector and an adversarial trainer. Instead of using non-differentiable substructure detection algorithms, we pre-train a differentiable convolutional neural network as the detector to approximate these detection algorithms. The adversarial trainer is to match the detected substructure of the reconstructed graph to the detected substructure of the original graph. Also, we provide an effective solution for the optimization problems. Moreover, we exploit the learned representations of users for the next activity type prediction. Finally, we present extensive experimental results to demonstrate the improved performances of the proposed method. | ||||||||||
