| 文献作者 | Jianxun Lian | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-06 | ||||||||
| 文献关键字 | cross-domain; NN; attention model; attentive model；NSVD; 用点击的item embedding 去表达user representation | ||||||||||
| 摘要描述 | Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learn- ing techniques, especially in visual computing and natural language understanding, more and more researchers, have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning- based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models. | ||||||||||
| 文献作者 | Weiyu Cheng | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-03 | ||||||||
| 文献关键字 | NCF; NSVD; 用item embedding表达user embedding, 反之亦可; 传统的user embedding + 用item embedding表达的user embedding; | ||||||||||
| 摘要描述 | Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual- embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual- embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation. | ||||||||||
| 文献作者 | Yong Liu | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-03 | ||||||||
| 文献关键字 | time; temporal; implicit feedback; Gaussian process | ||||||||||
| 摘要描述 | Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods. | ||||||||||
| 文献作者 | Wei Zhao | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-04-02 | ||||||||
| 文献关键字 | Gan; Generative Adversarial Network; reinforcement learning; LSTM; MF | ||||||||||
| 摘要描述 | Recommender systems provide users with ranked lists of items based on individual’s preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term mod- els represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users’ interests and changing dynamics of items’ attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short- Term Information in top-n reCommendation us- ing adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used datasets.1 | ||||||||||
| 文献作者 | Liang Hu | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-03-13 | ||||||||
| 文献关键字 | attention; 绘图 | ||||||||||
| 摘要描述 | New contents like blogs and online videos are produced in every second in the new media age. We argue that attraction is one of the decisive factors for user selection of new contents. However, collaborative filtering cannot work without user feedback; and the existing content-based recommender systems are ineligible to capture and interpret the attractive points on new contents. Accordingly, we propose attraction modeling to learn and interpret user attractiveness. Specially, we build a multi-level attraction model (MLAM) over the content features—the story (textual data) and cast members (categorical data) of movies. In particular, we design multilevel personal filters to calculate users’ attractiveness on words, sentences and cast members at different levels. The experimental results show the superiority of MLAM over the state-of-the-art methods. In addition, a case study is provided to demonstrate the interpretability of MLAM by visualizing user attractiveness on a movie. | ||||||||||
| 文献作者 | Hao Wang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-03-05 | ||||||||
| 文献关键字 | physical distance; 一个点两个向量；非对称；asymmetric； 时间或者地理距离信号影响 | ||||||||||
| 摘要描述 | Point-of-Interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo- susceptibility of POI, and their physical distance. Geo-influence captures POI’s capacity at exerting geographical influence to other POIs, and geo- susceptibility reflects POI’s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation. | ||||||||||
| 文献作者 | |||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2019-03-04 | ||||||||
| 文献关键字 | self-attention | ||||||||||
| 摘要描述 | Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few- shot image classification are defined on sets of instances. Since solutions to such problems do not depend on the order of elements of the set, models used to address them should be permutation invariant. We present an attention-based neural network module, the Set Transformer, specifically designed to model interactions among elements in the input set. The model consists of an encoder and a decoder, both of which rely on attention mechanisms. In an effort to reduce computational complexity, we introduce an attention scheme inspired by inducing point methods from sparse Gaussian process literature. It reduces the computation time of self-attention from quadratic to linear in the number of elements in the set. We show that our model is theoretically attractive and we evaluate it on a range of tasks, demonstrating the state-of-the-art performance compared to recent methods for set-structured data. | ||||||||||
| 文献作者 | Huan Zhao and Quanming Yao and Jianda Li and Yangqiu Song and Dik Lun Lee | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2019-03-03 | ||||||||
| 文献关键字 | Meta-Graph; path-representation | ||||||||||
| 摘要描述 | Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose a group lasso regularized FM to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms. | ||||||||||
| 文献作者 | Xiaolong Wang | ||||||||||
| 文献发表年限 | 2017 | 创建时间 | 2019-02-28 | ||||||||
| 文献关键字 | nonlocal | ||||||||||
| 摘要描述 | Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non- local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code will be made available. | ||||||||||
| 文献作者 | Weizhi Ma | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2019-02-26 | ||||||||
| 文献关键字 | content; cnn; rating predition; combination; SDAE; 简单学习问题绘图 | ||||||||||
| 摘要描述 | Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users’ preferences in the target domain. Thought to be unrelated, users’ off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user’s off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user’s off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation. | ||||||||||
