Explainable recommendation: when design meets trust calibration

World Wide Web - Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent...     

融合主题信息和卷积神经网络的混合推荐算法

针对传统的协同过滤算法中数据稀疏和推荐结果不准确的问题,提出了一种基于隐狄利克雷分布（LDA）与卷积神经网络（CNN）的概率矩阵分解推荐模型（LCPMF）,该模型综合考虑项目评论文档的主题信息与深层语义信息。首先,分别使用LDA主题模型和文本CNN对项目评论文档建模;然后,获取项目评论文档的显著潜在低维主题信息及全局深层语义信息,从而捕获项目文档的多层次特征表示;最后,将得到的用户和多层次的项目特征融合到概率矩阵分解（PMF）模型中,产生预测评分进行推荐。在真实数据集Movielens 1M、Movielens 10M与Amazon上,将LCPMF与经典的PMF、协同深度学习（CDL）、卷积矩阵因子分解模型（ConvMF）模型进行对比。实验结果表明,相较PMF、CDL、ConvMF模型,所提推荐模型LCPMF的均方根误差（RMSE）和平均绝对误差（MAE）在Movielens 1M数据集上分别降低了6.03%和5.38%、5.12%和4.03%、1.46%和2.00%,在Movielens 10M数据集上分别降低了5.35%和5.67%、2.50%和3.64%、1.75%和1.74%,在Amazon数据集上分别降低17.71%和23.63%、14.92%和17.47%、3.51%和4.87%,验证了所提模型在推荐系统中的可行性与有效性。     

间接交互信息与因式分解机融合的推荐方法研究

针对现有推荐方法存在交互信息应用不充分和推荐性能不佳的问题,充分利用用户和项目之间的间接交互信息,采用可达矩阵来表达用户和项目之间的间接交互关系,通过可达矩阵与因式分解机有机融合,构建了一个新的推荐方法.在Amazon-Book、Last-FM和Yelp2018数据集上的实验表明,所提方法在推荐效果上既优于传统的基于因式分解机的推荐方法,又好于最新的基于神经网络因式分解机的推荐模型,在推荐的时间效率上比基于知识图谱注意力网络的推荐方法具有明显优势.同时,相对其他推荐方法,该方法还具有更好的可解释性.     

融合上下文信息的深度推荐模型

目前,在基于文档信息的推荐任务中,传统基于文档的混合推荐算法仍依赖于浅层的线性模型,当评分数据变得庞大且复杂时,其推荐性能往往不太理想。针对此问题,提出一种深度融合模型(DeepFM),该模型能够在完全捕获文本信息的同时也能很好地处理复杂且稀疏的评分数据。DeepFM由两个并行的神经网络组成,其中一路神经网络使用多层感知器提取评分矩阵的行向量信息从而获得用户的潜在特征向量,另一路则使用MLP和卷积神经网络(CNN)共同建模从而提取额外有关项目的文本信息得到项目潜在特征向量。最后,通过构建融合层将用户特征向量和项目特征向量进行融合得出预测评分。实验结果表明,DeepFM在MovieLens数据集和亚马逊数据集上的性能优于主流的推荐模型。     

Flickr group recommendation with auxiliary information in heterogeneous information networks

Over the past few years, the appropriate utilization of user communities or image groups in social networks (i.e., Flickr or Facebook) has drawn a great deal of attention. In this paper, we are particularly interested in recommending preferred groups to users who may favor according to auxiliary information. In real world, the images captured by mobile equipments explicitly record a lot of contextual information (e.g., locations) about users generating images. Meanwhile, several words are employed to describe the particular theme of each group (e.g., “Dogs for Fun Photos” image group in Flickr), and the words may mention particular entities as well as their belonging categories (e.g., “Animal”). In fact, the group recommendation can be conducted in heterogeneous information networks, where informative cues are in general multi-typed. Motivated by the assumption that the auxiliary information (visual features of images, mobile contextual information and entity-category information of groups in this paper) in heterogeneous information networks will boost the performance of the group recommendation, this paper proposes to combine auxiliary information with implicit user feedback for group recommendation. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user–user similarity via visual features and heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation approaches.     

基于内容的推荐与协同过滤融合的新闻推荐方法

针对基于内容的新闻推荐方法中用户兴趣多样性的缺乏问题和混合推荐方法存在的冷启动问题,提出一种基于内容与协同过滤融合的方法进行新闻推荐。首先利用基于内容的方法发现用户既有兴趣;再用内容与行为的混合相似度模式,寻找目标用户的相似用户群,预测用户对特征词的兴趣度,发现用户潜在兴趣;然后将用户既有兴趣与潜在兴趣融合,得到兼具个性化和多样性的用户兴趣模型;最后将候选新闻与融合模型进行相似度计算,形成推荐列表。实验结果显示,与基于内容的推荐方法相比,所提方法的F-measure和整体多样性Diversity均有明显提高;与混合推荐方法相比,性能相当,但候选新闻无需耗时积累足够的用户点击量,不存在冷启动问题。     

Explainable image classification with evidence counterfactual

Pattern Analysis and Applications - The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A...     

注入注意力机制的深度特征融合新闻推荐模型

现有新闻推荐模型在挖掘新闻特征和用户特征时,往往没有考虑所浏览新闻之间的关系、时序变化以及不同新闻对用户的重要性,从而缺乏全面性;同时,现有模型在新闻更细粒度的内容特征挖掘方面有欠缺.因此构建了一个能够全面而不冗余地进行用户表征并能提取新闻更细粒度片段特征的新闻推荐模型——注入注意力机制的深度特征融合新闻推荐模型.该模...     

关联文本信息的网络表示学习推荐算法

通过基于随机游走的网络表示学习算法得到节点的低维嵌入向量,进而将其应用于推荐系统是推荐领域很流行的研究方向.针对当前基于随机游走的网络表示学习算法仅着重考虑了网络结构特性而忽略文本信息的问题,提出一种关联文本信息的网络表示学习推荐算法.首先在随机游走阶段,考虑到了节点文本间的相似度,联合结构和文本信息对下一游走节点进行...     

GSIRec: Learning with graph side information for recommendation

World Wide Web - Collaborative filtering (CF) is one of the dominant techniques used in modern recommender systems. Traditional CF-based methods suffer from issues of data sparsity and cold start....     

Video recommendation over multiple information sources

Video recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user’s relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach.     

Cross attention fusion for knowledge graph optimized recommendation

Knowledge Graph has attracted a wide range of attention in the field of recommendation, which is usually applied as auxiliary information to solve the problem of data sparsity. However, most recommendation models cannot effectively mine the associations between the items to be recommended and the entities in the Knowledge Graph. In this paper, we propose CAKR, a knowledge graph recommendation method based on the cross attention unit, which is similar to MKR, a multi-task feature learning general framework that uses knowledge graph embedding tasks to assist recommendation tasks. Specifically, we design a new method to optimize the feature interaction between the items and the corresponding entities in the Knowledge Graph and propose a feature cross-unit combined with the attention mechanism to enhance the recommendation effect. Through extensive experiments on the public datasets of movies, books, and music, we prove that CAKR is better than MKR and other knowledge graph recommendation methods so that the new feature cross-unit designed in this paper is effective in improving the accuracy of the recommendation system.

     

多层结合的多阶显式交互的融合推荐模型

现有不少模型着眼于对有限数据通过生成显式特征交互以进行挖掘来提升点击率预测效果,但存在以下问题：对于原特征与新生成的显式特征,直接一起输入到一个统一的神经网络结构进行挖掘然后输出,由于两者参数量差别较大导致在表征上差异巨大;同时如果直接采用多级层数的神经网络结构还会导致低层,如第一、二层信息的丢失,但若直接将各层进行累加以结合,则一些层中有用性有限的信息可能成为噪声以影响预测。为此设计多层权重结合的多阶显式交互的融合推荐模型,通过将原数据与生成的多阶显式特征分别放入各自对应的自注意力层中挖掘,其中各自对应结构的层数不同,同时对各层进行加权后输出以完成多层的结合,以提高点击率预测效果。通过在两个公开数据集上与多个不同模型进行比较分析,并对模型进行消融对比和超参数对比实验,证明了该模型能有效挖掘原特征与显式交互特征信息,平衡各阶特征表征能力。     

Trust-aware recommendation based on heterogeneous multi-relational graphs fusion

Users’ trust relations have a significant influence on their choice towards different products. However, few recommendation or prediction algorithms both consider users’ social trust relations and item-related knowledge, which makes them difficult to cope with cold start and the data sparsity problems. In this paper, we propose a novel trust-ware recommendation method based on heterogeneous multi-relational graphs fusion, termed as T-MRGF. In contrast with other traditional methods, it fuses the user-related and item-related graphs with the user–item interaction graph and fully utilizes the high-level connections existing in heterogeneous graphs. Specifically, we first establish the user–user trust relation graph, user–item interaction graph and item–item knowledge graph, and the user feature and item feature, which have been obtained from the user–item graph, are used as the input of the user-related graph and the item-related graph respectively. The fusion is achieved through the cascade of feature vectors before and after feature propagation. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Simulation results show that the proposed method significantly improve the recommendation performance compared to the state-of-the-art KG-based algorithms both in accuracy and training efficiency.     

融合朋友关系和标签信息的张量分解推荐算法

针对大众标注网站项目推荐系统中存在数据矩阵稀疏性影响推荐效果的问题,考虑矩阵奇异值分解(SVD)能有效地平滑数据矩阵中的数据,以及朋友圈能够反映出一个人的兴趣爱好,提出了一种融合朋友关系和标签信息的张量分解推荐算法。首先,利用高阶奇异值分解(HOSVD)方法对用户-项目-标签三元组信息进行潜在语义分析和多路降维,分析用户、项目、标签三者间关系;然后,再结合用户朋友关系、朋友间相似度,修正张量分解结果,建立三阶张量模型,从而实现推荐。该模型方法在两个真实数据集上进行了实验,结果表明,所提算法与高阶奇异值分解的方法比较,在推荐的召回率和精确度指标上分别提高了2.5%和4%,因此,所提算法进一步验证了结合朋友关系能够提高推荐的准确率,并扩展了张量分解模型,实现用户个性化推荐。     

卷积融合文本和异质信息网络的学术论文推荐算法

针对交互数据稀疏和推荐多样性问题,基于卷积协同过滤推荐框架提出卷积融合文本和异质信息网络的学术论文推荐算法（WN-APR）。首先学习不同语义下用户和论文的多样化特征,缓解数据稀疏问题;然后基于外积设计不同语义特征相互增强的方式融合它们,并使用三维卷积神经网络代替二维卷积神经网络充分挖掘不同特征对性能的影响;最后改进贝叶斯个性化排序损失函数增强推荐多样性。在CiteuLike-a、CiteuLike-t数据集上的实验结果表明,相比于基线模型,WN-APR在准确率和多样性的四个指标上都有所提升。     

Heterogeneous information network-based interest composition with graph neural network for recommendation

Applied Intelligence - Heterogeneous information networks (HINs) are widely applied to recommendation systems due to their capability of modeling various auxiliary information with meta-paths....     

个性化信息推荐方法研究

随着信息技术和互联网的发展,人们进入了信息过量且愈发碎片化的时代。当前,个性化信息推送是用户获取网络信息的有效渠道。由于信息的更新速度快和用户兴趣更新等问题,传统的推荐算法很少关注甚至忽略上述因素,造成最终的推荐结果欠佳。为了给用户更好的个性化推荐服务,论文首次引入截取因子,提出了组合推荐算法（CR算法）。该算法的实质是将截取因子引入到基于内容的推荐算法与基于用户的协同过滤算法中,进而生成混合推荐算法。在推荐列表中,CR算法产生的推荐结果由两部分组成:一部分由混合推荐算法生成,另一部分由基于用户的协同过滤算法生成。根据信息的发布时间,决定该信息由哪类算法产生推荐:当浏览时间与当前时间的间隔不大于某个值时,采用混合推荐算法;否则,直接采用基于用户的协同过滤算法。基于真实数据的实验结果表明,CR算法优于同类算法。