| 文献作者 | Bal´azs Hidasi | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2019-11-05 | ||||||||
| 文献关键字 | GRU4Rec-basic | ||||||||||
| 摘要描述 | We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNNbased approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches. | ||||||||||
| 文献作者 | Lei Guo | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-11-05 | ||||||||
| 文献关键字 | Session Recommendation; Streaming Recommendation; Attention Model; Matrix Factorization； GRU；paired sample t-test; 配对样本t检验 | ||||||||||
| 摘要描述 | Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. Recent studies address (1) by exploiting the attention mechanism in Recurrent Neural Network (RNN) to better model the user’s current intent, which leads to promising improvements. However, the proposed attention models are based solely on the current session. Moreover, existing studies only perform SR under static offline settings and none of them explore (2). In this work, we target SSR and propose a Streaming Sessionbased Recommendation Machine (SSRM) to tackle these two challenges. Specifically, to better understand the uncertainty of user behaviors, we propose a Matrix Factorization (MF) based attention model, which improves the commonly used attention mechanism by leveraging the user’s historical interactions. To deal with the largevolume and high-velocity challenge, we introduce a reservoir-based streaming model where an active sampling strategy is proposed to improve the efficiency of model updating. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of the SSRM method compared to several state-of-the-art methods in terms of MRR and Recall. | ||||||||||
| 文献作者 | Liang hu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-10-14 | ||||||||
| 文献关键字 | purpose; session-based; ijcai | ||||||||||
| 摘要描述 | A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subset have strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. To address the shortcomings in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item and assign them into the corresponding channels. Moreover, a purpose-specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity. | ||||||||||
| 文献作者 | Zibin Zheng; Xiangnan He | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-09-27 | ||||||||
| 文献关键字 | attention nn; ijcai；aggregation operation | ||||||||||
| 摘要描述 | Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle. In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle’s representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM. | ||||||||||
| 文献作者 | Chen Ma | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-08-21 | ||||||||
| 文献关键字 | 组织set（session）中的item embeddings | ||||||||||
| 摘要描述 | The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation. | ||||||||||
| 文献作者 | Bo Du | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-08-20 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | Despite the great success of many matrix factorization based collaborative filtering approaches, there is still much space for improvement in recommender system field. One main obstacle is the cold-start and data sparseness problem, requiring better solutions. Recent studies have attempted to integrate review information into rating prediction. However, there are two main problems: (1) most of existing works utilize a static and independent method to extract the latent feature representation of user and item reviews ignoring the correlation between the latent features, which may fail to capture the preference of users comprehensively. (2) there is no effective framework that unifies ratings and reviews. Therefore, we propose a novel dual attention mutual learning between ratings and reviews for item recommendation, named DAML. Specifically, we utilize local and mutual attention of the convolutional neural network to jointly learn the features of reviews to enhance the interpretability of the proposed DAML model. Then the rating features and review features are integrated into a unified neural network model, and the higher-order nonlinear interaction of features are realized by the neural factorization machines to complete the final rating prediction. Experiments on the five real-world datasets show that DAML achieves significantly better rating prediction accuracy compared to the state-of-the-art methods. Furthermore, the attention mechanism can highlight the relevant information in reviews to increase the interpretability of rating prediction. | ||||||||||
| 文献作者 | Xiaofeng Gao | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-08-20 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | Sequential recommendation and information dissemination are two traditional problems for sequential information retrieval. The common goal of the two problems is to predict future user-item interactions based on past observed interactions. The difference is that the former deals with users’ histories of clicked items, while the latter focuses on items’ histories of infected users. In this paper, we take a fresh view and propose dual sequential prediction models that unify these two thinking paradigms. One user-centered model takes a user’s historical sequence of interactions as input, captures the user’s dynamic states, and approximates the conditional probability of the next interaction for a given item based on the user’s past clicking logs. By contrast, one item-centered model leverages an item’s history, captures the item’s dynamic states, and approximates the conditional probability of the next interaction for a given user based on the item’s past infection records. To take advantage of the dual information, we design a new training mechanism which lets the two models play a game with each other and use the predicted score from the opponent to design a feedback signal to guide the training. We show that the dual models can better distinguish false negative samples and true negative samples compared with single sequential recommendation or information dissemination models. Experiments on four real-world datasets demonstrate the superiority of proposed model over some strong baselines as well as the effectiveness of dual training mechanism between two models. | ||||||||||
| 文献作者 | Jin Huang; Wayne Xin Zhao | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-08-12 | ||||||||
| 文献关键字 | Sequential recommendation, multi-hop reasoning, taxonomy, category; memory model； wsdm 19 | ||||||||||
| 摘要描述 | In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained pref- erence representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability. | ||||||||||
| 文献作者 | Wang-Cheng Kang, Julian McAuley | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-08-06 | ||||||||
| 文献关键字 | ICDM；dropout；残差处理；点乘对称性；复杂度分析；内存分析（参数数量）；神经网络技巧（残差输入） | ||||||||||
| 摘要描述 | Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the ‘con- text’ of users’ activities on the basis of actions they have performed recently. To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Markov Chains assume that a user’s next action can be predicted on the basis of just their last (or last few) actions, while RNNs in principle allow for longer-term semantics to be uncovered. Generally speaking, MC-based methods perform best in extremely sparse datasets, where model parsimony is critical, while RNNs perform better in denser datasets where higher model complexity is affordable. The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC). At each time step, SASRec seeks to identify which items are ‘relevant’ from a user’s action history, and use them to predict the next item. Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets. Moreover, the model is an order of magnitude more efficient than comparable CNN/RNN-based models. Visual- izations on attention weights also show how our model adaptively handles datasets with various density, and uncovers meaningful patterns in activity sequences. | ||||||||||
| 文献作者 | JunlinZhang | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-08-06 | ||||||||
| 文献关键字 | attention; second-order; FFM | ||||||||||
| 摘要描述 | Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning-based model and attention mechanism in various tasks in computer vision (CV) and natural language processing (NLP). How to combine the attention mechanism with deep CTR model is a promising direction because it may ensemble the advantages of both sides. Although some CTR model such as Attentional Factorization Machine (AFM) has been proposed to model the weight of second order interaction features, we posit the evaluation of feature importance before explicit feature interaction procedure is also important for CTR prediction tasks because the model can learn to selectively highlight the informative features and suppress less useful ones if the task has many input features. In this paper, we propose a new neural CTR model named Field Attentive Deep Field- aware Factorization Machine (FAT-DeepFFM) by combining the Deep Field-aware Factorization Machine (DeepFFM) with Compose-Excitation network (CENet) field attention mechanism which is proposed by us as an enhanced version of Squeeze- Excitation network (SENet) to highlight the feature importance. We conduct extensive experiments on two real-world datasets and the experiment results show that FAT-DeepFFM achieves the best perfor- mance and obtains different improvements over the state-of-the-art methods. We also compare two kinds of attention mechanisms (attention before ex- plicit feature interaction vs. attention after explicit feature interaction) and demonstrate that the former one outperforms the latter one significantly. | ||||||||||
