| 文献作者 | Jingyue Gao; Xing Xie | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-27 | ||||||||
| 文献关键字 | attention/attentive model； review information; explicit feature hierarchy (hierarchical); Microsoft Concept Graph; 雇人验证算法解释性好坏；算法鲁棒性robust验证（对用户分组验证结果不变） | ||||||||||
| 摘要描述 | Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing explainable methods. The basic idea is to build an initial network based on an explainable deep hierarchy (e.g., Microsoft Concept Graph) and improve the model accuracy by optimizing key variables in the hierarchy (e.g., node importance and relevance). To ensure accurate rating prediction, we propose an attentive multi-view learning framework. The framework enables us to handle sparse and noisy data by co-regularizing among different feature levels and combining predictions attentively. To mine readable explanations from the hierarchy, we formulate personalized explanation generation as a constrained tree node selection problem and propose a dynamic programming algorithm to solve it. Experimental results show that our model outperforms state-of-the-art methods in terms of both accuracy and explainability. | ||||||||||
| 文献作者 | Lu Yu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-26 | ||||||||
| 文献关键字 | 物品和物品之间的关系通过weight表示出来（解释）；residual network ResNet; 讨论了temporary order在同一个session中的不重要性；attention model weight新求法； Yelp; Amazon; Movies&TV; CDs&Vinyl | ||||||||||
| 摘要描述 | In modern e-commerce, the temporal order behind users’ transactions implies the importance of exploiting the transition dependency among items for better inferring what a user prefers to interact in “near future”. The types of interaction among items are usually divided into individual-level interaction that can stand out the transition order between a pair of items, or union-level relation between a set of items and single one. However, most of existing work only captures one of them from a single view, especially on modeling the individual-level interaction. In this paper, we propose a Multi-order Attentive Ranking Model (MARank) to unify both individual- and union-level item interaction into preference inference model from multiple views. The idea is to represent user’s short-term preference by embedding user himself and a set of present items into multi-order features from intermedia hidden status of a deep neural network. With the help of attention mechanism, we can obtain a unified embedding to keep the individual-level interactions with a linear combination of mapped items’ features. Then, we feed the aggregated embedding to a designed residual neural network to capture union-level interaction. Thorough experiments are conducted to show the features of MARank under various component settings. Furthermore experimental results on several public datasets show that MARank significantly outperforms the state-of-the-art baselines on different evaluation metrics. The source code can be found at https://github.com/voladorlu/MARank. | ||||||||||
| 文献作者 | Liang Hu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-25 | ||||||||
| 文献关键字 | 通过gate neural network自动学习加权参加；为边建立vector representation；类似于OD pair一样将graph中的边也表示成vector；user-user; item-item; user-item | ||||||||||
| 摘要描述 | Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item inter- action is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces im- portant yet challenging issues, including modeling heteroge- neous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to ag- gregate the user-user/item-item relations within a given con- text as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by consider- ing user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommen- dation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the inter- pretability of modeling influential contexts in explaining the recommendation results. | ||||||||||
| 文献作者 | Yu Zhu | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2019-04-24 | ||||||||
| 文献关键字 | explicit time interval; time signal; LSTM; Phased LSTM; 融入时间信息； time interval2vec | ||||||||||
| 摘要描述 | Recently, Recurrent Neural Network (RNN) solutions for recommender systems (RS) are becoming increasingly popular. The insight is that, there exist some intrinsic patterns in the sequence of users’ actions, and RNN has been proved to perform excellently when modeling sequential data. In traditional tasks such as language modeling, RNN solutions usually only consider the sequential order of objects without the notion of interval. However, in RS, time intervals between users’ actions are of significant importance in capturing the relations of users’ actions and the traditional RNN architectures are not good at modeling them. In this paper, we propose a new LSTM variant, i.e. Time-LSTM, to model users’ sequential actions. Time-LSTM equips LSTM with time gates to model time intervals. These time gates are specifically designed, so that compared to the traditional RNN solutions, Time-LSTM better captures both of users’ short- term and long-term interests, so as to improve the recommendation performance. Experimental results on two real-world datasets show the superiority of the recommendation method using Time- LSTM over the traditional methods. | ||||||||||
| 文献作者 | Hao Liu, Ting Li, Renjun Hu, Yanjie Fu, Jingjing Gu, Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-23 | ||||||||
| 文献关键字 | trans2vec; metric learning; graph; embedding; | ||||||||||
| 摘要描述 | Multi-modal transportation recommendation has a goal of recommending a travel plan which considers various transportation modes, such as walking, cycling, automobile, and public transit, and how to connect among these modes. The successful development of multi-modal transportation recommendation systems can help to satisfy the diversified needs of travelers and improve the efficiency of transport networks. However, existing transport recommender systems mainly focus on unimodal transport planning. To this end, in this paper, we propose a joint representation learning framework for multi-modal transportation recommendation based on a carefully-constructed multi-modal transportation graph. Specifically, we first extract a multi-modal transportation graph from large-scale map query data to describe the concurrency of users, Origin-Destination (OD) pairs, and transport modes. Then, we provide effective solutions for the optimization problem and develop an anchor embedding for transport modes to initialize the embeddings of transport modes. Moreover, we infer user relevance and OD pair relevance, and incorporate them to regularize the representation learning. Finally, we exploit the learned representations for online multi-modal transportation recommendations. Indeed, our method has been deployed into one of the largest navigation Apps to serve hundreds of millions of users, and extensive experimental results with real-world map query data demonstrate the enhanced performance of the proposed method for multi-modal transportation recommendations. | ||||||||||
| 文献作者 | Travis Ebesu； Bin Shen； Yi Fang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-22 | ||||||||
| 文献关键字 | Augmented; Memory Network; attention model | ||||||||||
| 摘要描述 | Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learn- ing has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model’s recommendation process and suggest the presence of higher order interactions. | ||||||||||
| 文献作者 | Xu Chen； Yongfeng Zhang | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-22 | ||||||||
| 文献关键字 | CNN; GRU; Time-aware GRU； from static to dynamic explainable； attention mechinism; 权重可视化 visualization；可解释； interpret; Neural Attentive Model for Explainable Recommendation by Learning User Dynamic Preference | ||||||||||
| 摘要描述 | Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, in real scenarios, a user’s preference is always dynamic, and she may be interested in different product features at different states. The mismatching between the explanation and user preference may degrade costumers’ satisfaction, confidence and trust for the recommender system. With the desire to fill up this gap, in this paper, we build a novel Dynamic Explainable Recommender (called DER) for more accurate user modeling and explanations. In specific, we design a time-aware gated recurrent unit (GRU) to model user dynamic preferences, and profile an item by its review information based on sentence-level convolutional neural network (CNN). By attentively learning the important review information according to the user current state, we are not only able to improve the recommendation performance, but also can provide explanations tailored for the users’ current preferences. We conduct extensive experiments to demonstrate the superiority of our model for improving recommendation performance. And to evaluate the explainability of our model, we first present examples to provide intuitive analysis on the highlighted review information, and then crowd-sourcing based evaluations are conducted to quantitatively verify our model’s superiority. | ||||||||||
| 文献作者 | Jingjing Li | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-13 | ||||||||
| 文献关键字 | encoder; decoder; content-based; cold-start; Symmetric recovery; projection; lowe-rank; sparsity | ||||||||||
| 摘要描述 | Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are inde- pendently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Low- rank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and comput- ing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user at- tributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a signif- icant performance improvement compared with several con- ventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well. | ||||||||||
| 文献作者 | Junyuan Shang | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-13 | ||||||||
| 文献关键字 | Memory Network; Graph model; RNN; EHR; DDI; longitudinal hidden state; 图模型结合Memory network模型 | ||||||||||
| 摘要描述 | Recent progress in deep learning is revolutionizing the health- care domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data. | ||||||||||
| 文献作者 | Wenjing Fu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-13 | ||||||||
| 文献关键字 | content-based; review-based; score-based; SDAE; aSDAE; deep learning; cross-domain | ||||||||||
| 摘要描述 | As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, cross-domain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for cross-domain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods. | ||||||||||
