| 文献作者 | Yanan Xu | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-07-30 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | The large volume and variety of apps pose a great challenge for people to choose appropriate apps. As a consequence, app recommendation is becoming increasingly important. Recently, app usage data which record the sequence of apps being used by a user have become increasingly available. Such data record the usage context of each instance of app use, i.e., the app instances being used together with this app (within a short time window). Our empirical data analysis shows that a user has a pattern of app usage contexts. More importantly, the similarity in the two users’ preferences over mobile apps is correlated with the similarity in their app usage context patterns. Inspired by these important observations, this paper tries to leverage the predictive power of app usage context patterns for effective app recommendation. To this end, we propose a novel neural approach which learns the embeddings of both users and apps and then predicts a user’s preference for a given app. Our neural network structure models both a user’s preference over apps and the user’s app usage context pattern in a unified way. To address the issue of unbalanced training data, we introduce several sampling methods to sample user-app interactions and app usage contexts effectively. We conduct extensive experiments using a large real app usage data. Comparative results demonstrate that our approach achieves higher precision and recall, compared with the state-of-the-art recommendation methods. | ||||||||||
| 文献作者 | Xu Chen; Yongfeng Zhang | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-07-27 | ||||||||
| 文献关键字 | Attention Network; 不同用户关注到的图片都parts可能会不一样；有些只关心部分，有些就会关心整体； 部分和整体 | ||||||||||
| 摘要描述 | Fashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner. | ||||||||||
| 文献作者 | Pengfei Wang | ||||||||||
| 文献发表年限 | 2015 | 创建时间 | 2019-05-17 | ||||||||
| 文献关键字 | HRM; aggregation operation; next basket | ||||||||||
| 摘要描述 | Next basket recommendation is a crucial task in market basket analysis. Given a user’s purchase history, usually a sequence of transaction data, one attempts to build a recommender that can predict the next few items that the user most probably would like. Ideally, a good recommender should be able to explore the sequential behavior (i.e., buy- ing one item leads to buying another next), as well as account for users’ general taste (i.e., what items a user is typically interested in) for recommendation. Moreover, these two factors may interact with each other to influence users’ next purchase. To tackle the above problems, in this paper, we introduce a novel recommendation approach, namely hierarchical representation model (HRM). HRM can well capture both sequential behavior and users’ general taste by involving transaction and user representations in prediction. Meanwhile, the flexibility of applying different aggregation operations, especially nonlinear operations, on representations allows us to model complicated interactions among different factors. Theoretically, we show that our model subsumes several existing methods when choosing proper aggregation operations. Empirically, we demonstrate that our model can consistently outperform the state-of-the-art baselines under different evaluation metrics on real-world transaction data. | ||||||||||
| 文献作者 | George Karypis | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-05-02 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | Users’ behaviors are driven by their preferences across various aspects of items they are potentially interested in purchasing, view- ing, etc. Latent space approaches model these aspects in the form of latent factors. Although such approaches have been shown to lead to good results, the aspects that are important to different users can vary. In many domains, there may be a set of aspects for which all users care about and a set of aspects that are specific to different subsets of users. To explicitly capture this, we consider models in which there are some latent factors that capture the shared aspects and some user subset specific latent factors that capture the set of aspects that the different subsets of users care about. In particular, we propose two latent space models: rGLSVD and sGLSVD, that combine such a global and user subset specific sets of latent factors. The rGLSVD model assigns the users into different subsets based on their rating patterns and then estimates a global and a set of user subset specific local models whose number of latent dimensions can vary. The sGLSVD model estimates both global and user subset specific local models by keeping the number of latent dimensions the same among these models but optimizes the grouping of the users in order to achieve the best approximation. Our experiments on various real-world datasets show that the proposed approaches significantly outperform state-of-the-art latent space top-N recommendation approaches. | ||||||||||
| 文献作者 | Binbin Hu, Chuan Shi | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-05-01 | ||||||||
| 文献关键字 | Attention Mechanism； HIN; Graph | ||||||||||
| 摘要描述 | Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have two major shortcomings. First, these models seldom learn an explicit representation for path or meta-path in the recommendation task. Second, they do not consider the mutual effect between the meta-path and the involved user-item pair in an interaction. To address these issues, we develop a novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation. We elaborately design a three-way neural interaction model by explicitly incorporating meta-path based context. To construct the meta-path based context, we propose to use a priority based sampling technique to select high-quality path instances. Our model is able to learn effective representations for users, items and meta-path based context for implementing a powerful interaction function. The co-attention mechanism improves the representations for meta-path based context, users and items in a mutual enhancement way. Extensive experiments on three real-world datasets have demonstrated the effectiveness of the proposed model. In particular, the proposed model performs well in the cold-start scenario and has potentially good interpretability for the recommendation results. | ||||||||||
| 文献作者 | Rahul Bhagat | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-05-01 | ||||||||
| 文献关键字 | KDD; repeat recommendation; amazon | ||||||||||
| 摘要描述 | Repeat purchasing, i.e., a customer purchasing the same product multiple times, is a common phenomenon in retail. As more customers start purchasing consumable products (e.g., toothpastes, diapers, etc.) online, this phenomenon has also become prevalent in e-commerce. However, in January 2014, when we looked at pop- ular e-commerce websites, we did not find any customer-facing features that recommended products to customers from their purchase history to promote repeat purchasing. Also, we found limited research about repeat purchase recommendations and none that deals with the large scale purchase data that e-commerce web- sites collect. In this paper, we present the approach we developed for modeling repeat purchase recommendations. This work has demonstrated over 7% increase in the product click-through rate on the personalized recommendations page of the Amazon.com web- site and has resulted in the launch of several customer-facing features on the Amazon.com website, the Amazon mobile app, and other Amazon websites. | ||||||||||
| 文献作者 | Pengpeng Zhao; Yanchi Liu | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-29 | ||||||||
| 文献关键字 | time interval; distance interval; LSTM; Time-LSTM；减少LSTM参数的方法；CA; SIN; Gowalla; Brightkite | ||||||||||
| 摘要描述 | Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatiotemporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. In this pa- per, we propose a new variant of LSTM, named ST- LSTM, which implements time gates and distance gates into LSTM to capture the spatiotemporal relation between successive check-ins. Specifically, one-time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation. | ||||||||||
| 文献作者 | Shu Wu; | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-29 | ||||||||
| 文献关键字 | 融合一个关系已知的矩阵；Graph Neural Networks (GNN); attention model; long- and short-term; session-based; Yoochoose; Diginetica | ||||||||||
| 摘要描述 | The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embed- ding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graph-structured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently. | ||||||||||
| 文献作者 | Pengjie Ren | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-29 | ||||||||
| 文献关键字 | 所谓的repeat，指的是推荐可重复消费的物品；GRU；session encoder; attention model; OOCHOOSE; DIGINETICA; LASTFM | ||||||||||
| 摘要描述 | Recurrent neural networks for session-based recommendation have attracted a lot of attention recently because of their promising performance. repeat consumption is a common phenomenon in many recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), where the same item is re-consumed repeatedly over time. However, no previous studies have emphasized repeat consumption with neural networks. An effective neural approach is needed to decide when to perform repeat recommendation. In this paper, we incorporate a repeat-explore mechanism into neural networks and propose a new model, called RepeatNet, with an encoder-decoder structure. RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user’s history and recommends them at the right time. We report on extensive experiments on three benchmark datasets. RepeatNet outperforms state-of-the-art baselines on all three datasets in terms of MRR and Recall. Furthermore, as the dataset size and the repeat ratio increase, the improvements of RepeatNet over the baselines also increase, which demonstrates its advantage in handling repeat recommendation scenarios. | ||||||||||
| 文献作者 | Qiannan Zhu; | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-04-27 | ||||||||
| 文献关键字 | CNN; RNN; Attention Model; 不一样的objective function； content-based; dynamic; 图画的不错；Adressa-1week; Adressa-10week | ||||||||||
| 摘要描述 | With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem. Many existing recommendation methods that regard the recommendation procedure as the static process, have achieved better recommendation performance. However, they usually fail with the dynamic diversity of news and user’s interests, or ignore the importance of sequential information of user’s clicking selection. In this paper, taking full advantages of convolution neural network (CNN), recurrent neural network (RNN) and attention mechanism, we propose a deep attention neural network DAN for news recommendation. Our DAN model presents to use attention-based parallel CNN for aggregating user’s interest features and attention-based RNN for capturing rich- er hidden sequential features of user’s clicks, and combines these features for new recommendation. We conduct experiment on real-world news data sets, and the experimental results demonstrate the superiority and effectiveness of our proposed DAN model. | ||||||||||
