| 文献作者 | Disheng Dong | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-02-22 | ||||||||
| 文献关键字 | multi-task learning ; sequential (item-to-item relations) | ||||||||||
| 摘要描述 | General recommender and sequential recommender are two applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors, whereas sequential recommender focuses on exploring the item-to-item sequential relations, fail- ing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combining them. However, the existing approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model. Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of- the-art methods across the tasks of both sequential and general recommender. | ||||||||||
| 文献作者 | Jingtao Ding | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-02-22 | ||||||||
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| 摘要描述 | Most existing recommender systems leverage theprimary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed asImplicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and flexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efficiency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares(eALS) learner. Notably, our algorithm can efficiently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-worlddatasets demonstrate that our method outperforms several state-of-the-art MF methods by10%28:4%. Our implementation is available at: https://github.com/dingjingtao/ViewenhancedALS. | ||||||||||
| 文献作者 | Huang, Jizhou and Zhang, Wei and Sun, Yaming and Wang, Haifeng and Liu, Ting | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-02-21 | ||||||||
| 文献关键字 | Entity recommendation; Baidu； BiLSTM | ||||||||||
| 摘要描述 | Entity recommendation, providing search users with an improved experience by assisting themin finding related entities for a given query, has become an indispensable feature of today’s Websearch engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as “apple” because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements. | ||||||||||
| 文献作者 | Feng Zhu | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-02-16 | ||||||||
| 文献关键字 | cross-domain recommendation; partiall-overlapped; deep NN | ||||||||||
| 摘要描述 | |||||||||||
| 文献作者 | Jiaqi Ma | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-01-28 | ||||||||
| 文献关键字 | KDD 2018; multi-task learning; mixture of experts; neural network; recommendation system； Shared-Bottom model | ||||||||||
| 摘要描述 | Neural-based multi-task learning has been successfully used in many real-world large-scale applications such as recommendation systems. For example, in movie recommendations, beyond providing users movies which they tend to purchase and watch, the system might also optimize for users liking the movies afterwards. With multi-task learning, we aim to build a single model that learns these multiple goals and tasks simultaneously. However, the prediction quality of commonly used multi-task models is often sensitive to the relationships between tasks. It is therefore important to study the modeling tradeoffs between task-specific objectives and inter-task relationships. In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data. We adapt the Mixture-of- Experts (MoE) structure to multi-task learning by sharing the expert submodels across all tasks, while also having a gating network trained to optimize each task. To validate our approach on data with different levels of task relatedness, we first apply it to a synthetic dataset where we control the task relatedness. We show that the proposed approach performs better than baseline methods when the tasks are less related. We also show that the MMoE structure results in an additional trainability benefit, depending on different levels of randomness in the training data and model initialization. Furthermore, we demonstrate the performance improvements by MMoE on real tasks including a binary classification benchmark, and a large-scale content recommendation system at Google. | ||||||||||
| 文献作者 | Zhi Li | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-01-28 | ||||||||
| 文献关键字 | Next-item Recommendation, Sequential Behaviors, Item Embed- ding, Recurrent Neural Networks | ||||||||||
| 摘要描述 | |||||||||||
| 文献作者 | Shuai Zhang | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-01-23 | ||||||||
| 文献关键字 | Next item; metric learning； long- and short-term; time signal; 时间信号 | ||||||||||
| 摘要描述 | In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user’s historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user’s transient interests. The model is finally trained in a metric learning framework, taking both local and global user intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin. | ||||||||||
| 文献作者 | Liang Hu | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2018-12-19 | ||||||||
| 文献关键字 | session-based; diversity; wide-in-wide-out; Tmall; sequential | ||||||||||
| 摘要描述 | Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods. | ||||||||||
| 文献作者 | Edward Choi | ||||||||||
| 文献发表年限 | 2016 | 创建时间 | 2018-12-01 | ||||||||
| 文献关键字 | 2Vec; Representation Learning; Medical Concepts; Healthcare An- alytics; Neural Networks | ||||||||||
| 摘要描述 | Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits from Electronic Health Records (EHR) has broad applications in healthcare analytics. Patient EHR data consists of a sequence of visits over time, where each visit includes multiple medical concepts, e.g., diagnosis, procedure, and medication codes. This hierarchical structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within a visit. In this work, we propose Med2Vec , which not only learns the representations for both medical codes and visits from large EHR datasets with over million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec shows significant improvement in prediction accuracy in clinical applications compared to baselines such as Skip-gram, GloVe, and stacked autoencoder, while providing clinically meaningful interpretation. | ||||||||||
| 文献作者 | Xu Chen | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2018-10-11 | ||||||||
| 文献关键字 | Sequential Recommendation; Memory Networks; Collaborative Filtering | ||||||||||
| 摘要描述 | User preferences are usually dynamic in real-world recommender systems, and a user’s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms – including both shallow and deep approaches – usually embed a user’s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user’s historical records and future interests. In this paper, we aim to express, store, and manipulate users’ historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users’ historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users’ sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users’ future actions are affected by previous behaviors. | ||||||||||
