| 文献作者 | Fangyuan Luo , Jun Wu∗ , Tao Wang | ||||||||||
| 文献发表年限 | 2022 | 创建时间 | 2023-02-23 | ||||||||
| 文献关键字 | 离散化向量；海明相似度（向量计算法）；最优化排序指标 | ||||||||||
| 摘要描述 | We address the efficiency problem of personalized ranking from implicit feedback by hashing users and items with binary codes, so that top-N rec- ommendation can be fast executed in a Hamming space by bit operations. However, current hashing methods for top-N recommendation fail to align their learning objectives (such as pointwise or pair- wise loss) with the benchmark metrics for rank- ing quality (e.g. Average Precision, AP), resulting in sub-optimal accuracy. To this end, we propose a Discrete Listwise Personalized Ranking (DLPR) model that optimizes AP under discrete constraints for fast and accurate top-N recommendation. To resolve the challenging DLPR problem, we devise an efficient algorithm that can directly learn bi- nary codes in a relaxed continuous solution space. Specifically, theoretical analysis shows that the optimal solution to the relaxed continuous optimization problem is exactly the same as that of the original discrete DLPR problem. Through extensive experiments on two real-world datasets, we show that DLPR consistently surpasses state-of-the-art hashing methods for top-N recommendation. | ||||||||||
| 文献作者 | Shuyun Gu , Xiao Wang , Chuan Shi∗ and Ding Xiao | ||||||||||
| 文献发表年限 | 2022 | 创建时间 | 2022-12-06 | ||||||||
| 文献关键字 | GCN; Contrastive learning; Multi-behavior | ||||||||||
| 摘要描述 | Traditional recommendation usually focuses on uti- lizing only one target user behavior (e.g., purchase) but ignoring other auxiliary behaviors (e.g., click, add to cart). Early efforts of multi-behavior rec- ommendation often emphasize the differences be- tween multiple behaviors, i.e., they aim to extract useful information by distinguishing different be- haviors. However, the commonality between them, which reflects user’s common preference for items associated with different behaviors, is largely ig- nored. Meanwhile, the multi-behavior recommen- dation still severely suffers from limited supervi- sion signal issue. In this paper, we propose a novel self-supervised graph collaborative filtering model for multi-behavior recommendation named S-MBRec. Specifically, for each behavior, we ex- ecute the GCNs to learn the user and item em- beddings. Then we design a supervised task, dis- tinguishing the importance of different behaviors, to capture the differences between embeddings. Meanwhile, we propose a star-style contrastive learning task to capture the embedding common- ality between target and auxiliary behaviors, so as to alleviate the sparsity of supervision signal, re- duce the redundancy among auxiliary behavior, and extract the most critical information. Finally, we jointly optimize the above two tasks. Extensive experiments, in comparison with state-of-the-arts, well demonstrate the effectiveness of S-MBRec, where the maximum improvement can reach to 20%. | ||||||||||
| 文献作者 | Anjing Luo | ||||||||||
| 文献发表年限 | 2020 | 创建时间 | 2021-04-09 | ||||||||
| 文献关键字 | |||||||||||
| 摘要描述 | 考虑了会话中的物品同其他session之间的相似关系，并用相近的session表征来enrich当前会话中的物品表征。 | ||||||||||
| 文献作者 | Tong Xu; Hui Xiong | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2021-04-07 | ||||||||
| 文献关键字 | 主题模型；Topic Model; 显式主题；概率平滑；集合与集合关联进行统计；LDA | ||||||||||
| 摘要描述 | |||||||||||
| 文献作者 | |||||||||||
| 文献发表年限 | 2020 | 创建时间 | 2021-04-06 | ||||||||
| 文献关键字 | 隐式图结构学习；社交影响建模；离散化表征学习；Weisfeiler-Lehman；病毒传染路径构建 | ||||||||||
| 摘要描述 | |||||||||||
| 文献作者 | Tong Xu; Hui Xiong | ||||||||||
| 文献发表年限 | 2019 | 创建时间 | 2021-03-24 | ||||||||
| 文献关键字 | 用户组/社交影响；动态阈值学习；长期决定可能由于偏好、短期决定可能仅仅因为社交因素；优化函数设计类；非神经网络设计 | ||||||||||
| 摘要描述 | In this paper, we investigated how to exploit the dynamic mutual influence for enhancing the prediction of social event participation. A unique characteristic of our method is that the social influence is integrated into the threshold calculation for the discriminant function, which reflects the dynamic mutual dependence within friends for event participation. Specifically, we designed a variant two-stage discriminant framework to capture both users’ preferences and their latest social connections. | ||||||||||
| 文献作者 | Jin Huang | ||||||||||
| 文献发表年限 | 2018 | 创建时间 | 2021-01-21 | ||||||||
| 文献关键字 | 序列推荐系统+ 知识图谱 | ||||||||||
| 摘要描述 | With the revival of neural networks, many studies try to adapt powerful sequential neural models, i.e., Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledgeenhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information. | ||||||||||
| 文献作者 | Jianling Wang | ||||||||||
| 文献发表年限 | 2020 | 创建时间 | 2020-12-24 | ||||||||
| 文献关键字 | 假设检验; Sequential GCN; 基于会话的GCN；Residual Gating; hyperrec | ||||||||||
| 摘要描述 | |||||||||||
