融合关系挖掘与协同过滤的物品冷启动推荐算法

针对新物品缺乏(非完全冷启动)或没有(完全冷启动)评分信息,协同过滤无法为新物品进行个性化推荐的问题,文中提出融合关系挖掘与协同过滤的推荐算法.首先,利用关系挖掘提取物品关系特征,根据属性之间的多种二元关系构建关系属性,丰富可用属性信息.然后,提出基于关系挖掘的近邻选取方法,增加邻近物品的多样性.最后,融合协同过滤方法,同时解决完全和非完全新物品冷启动问题,实现新物品的个性化推荐.在两个真实数据集上的实验表明,文中方法可以系统解决推荐系统中新物品的冷启动问题.     

Typicality‐based collaborative filtering for book recommendation

Nowadays, personalized recommender system placed an important role to predict the customer needs, interest about particular product in various application domains, which is identified according to the product ratings. During this process, collaborative filtering (CF) has been utilized because it is one of familiar techniques in recommender systems. The conventional CF methods analyse historical interactions of user‐item pairs based on known ratings and then use these interactions to produce recommendations. The major challenge in CF is that it needs to calculate the similarity of each pair of users or items by observing the ratings of users on same item, whereas the typicality‐based CF determines the neighbours from user groups based on their typicality degree. Typicality‐based CF can predict the ratings of users with improved accuracy. However, to eliminate the cold start problem in the proposed recommender system, the demographic filtering method has been employed in addition to the typicality‐based CF. A weighted average scheme has been applied on the combined recommendation results of both typicality‐based CF and demographic‐based CF to produce the best recommendation result for the user. Thereby, the proposed system has been able to achieve a coverage ratio of more than 95%, which indicates that the system is able to provide better recommendation for the user from the available lot of products.     

A novel social network hybrid recommender system based on hypergraph topologic structure

With the advent and popularity of social network, more and more people like to share their experience in social network. However, network information is growing exponentially which leads to information overload. Recommender system is an effective way to solve this problem. The current research on recommender systems is mainly focused on research models and algorithms in social networks, and the social networks structure of recommender systems has not been analyzed thoroughly and the so-called cold start problem has not been resolved effectively. We in this paper propose a novel hybrid recommender system called Hybrid Matrix Factorization(HMF) model which uses hypergraph topology to describe and analyze the interior relation of social network in the system. More factors including contextual information, user feature, item feature and similarity of users ratings are all taken into account based on matrix factorization method. Extensive experimental evaluation on publicly available datasets demonstrate that the proposed hybrid recommender system outperforms the existing recommender systems in tackling cold start problem and dealing with sparse rating datasets. Our system also enjoys improved recommendation accuracy compared with several major existing recommendation approaches.     

Bayesian dual neural networks for recommendation

Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.     

Collaborative error-reflected models for cold-start recommender systems

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users to easily find useful information. One notable challenge in practical CF is the cold start problem, which can be divided into cold start items and cold start users. Traditional CF systems are typically unable to make good quality recommendations in the situation where users and items have few opinions. To address these issues, in this paper, we propose a unique method of building models derived from explicit ratings and we apply the models to CF recommender systems. The proposed method first predicts actual ratings and subsequently identifies prediction errors for each user. From this error information, pre-computed models, collectively called the error-reflected model, are built. We then apply the models to new predictions. Experimental results show that our approach obtains significant improvement in dealing with cold start problems, compared to existing work.     

基于多数据源和联合聚类的智能推荐

随着Internet的普及和电子商务的盛行,智能推荐系统也应运而生.协同推荐是目前公认为最好的一种推荐技术,但其存在着一些不足之处,如:稀疏性、可扩展性和冷启动问题.本文提出一种混合推荐技术来克服协同过滤的不足.首先,通过引入多个数据源对评价矩阵进行平滑填充来解决数据的稀疏性问题.其次,采用从用户和项目两方面进行联合聚类来提高系统的可扩展性和精度.实验结果证明,该方法在很大程度上较传统的协同过滤方法推荐精度高,且在线推荐的速度快.     

基于CNN-LFM模型的个性化推荐

评分数据的稀疏性和新物品的冷启动问题一直是阻碍推荐系统发展的难题。针对这些问题,利用物品的图像数据作为辅助信息以提高评分预测的准确性,提出一种基于卷积神经网络与隐语义模型的推荐模型(CNN-LFM)。CNN-LFM模型利用隐语义模型挖掘评分数据,获得用户和物品的潜在特征,其中物品的潜在特征会在卷积神经网络提取的图像特征的约束下不断完善。在真实数据集下进行实验,对结果的定量和定性分析表明CNN-LFM模型不存在新物品的冷启动问题,即使当评分数据十分稀疏时,其性能也远远优于其它推荐模型。     

Sequence-based trust in collaborative filtering for document recommendation

Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users' past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users' various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users' sequences of ratings on documents. The model considers two factors – time factor and document similarity – in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.     

Proposal of a recommender system simulator based on a small-world model

This article proposes the development of a software simulator that allows the user to evaluate algorithms for recommender systems. This simulator consists of agents, items, a recommender, a controller, and a recorder, and it locates the agents and allocates the items based on a small-world network. An agent plays the role of a user in the recommender system, and the recommender also plays a role in the system. The controller handles the simulation flow where (1) the recommender recommends items to agents based on the recommendation algorithm, (2) each agent evaluates the items based on the agents’ rating algorithm and using the attributes of each item and agent, and (3) the recorder obtains the results of the rating and evaluation measurements for the recommendation pertaining to such information as precision and recall. This article considers the background of the proposal and the architecture of the simulator.     

Exploiting Pre-Trained Network Embeddings for Recommendations in Social Networks

Recommender systems as one of the most efficient information filtering techniques have been widely studied in recent years. However, traditional recommender systems only utilize user-item rating matrix for recommendations, and the social connections and item sequential patterns are ignored. But in our real life, we always turn to our friends for recommendations, and often select the items that have similar sequential patterns. In order to overcome these challenges, many studies have taken social connections and sequential information into account to enhance recommender systems. Although these existing studies have achieved good results, most of them regard social influence and sequential information as regularization terms, and the deep structure hidden in social networks and rating patterns has not been fully explored. On the other hand, neural network based embedding methods have shown their power in many recommendation tasks with their ability to extract high-level representations from raw data. Motivated by the above observations, we take the advantage of network embedding techniques and propose an embedding-based recommendation method, which is composed of the embedding model and the collaborative filtering model. Specifically, to exploit the deep structure hidden in social networks and rating patterns, a neural network based embedding model is first pre-trained, where the external user and item representations are extracted. Then, we incorporate these extracted factors into a collaborative filtering model by fusing them with latent factors linearly, where our method not only can leverage the external information to enhance recommendation, but also can exploit the advantage of collaborative filtering techniques. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method and the importance of these external extracted factors.     

An algorithm for efficient privacy-preserving item-based collaborative filtering

Collaborative filtering (CF) methods are widely adopted by existing recommender systems, which can analyze and predict user “ratings” or “preferences” of newly generated items based on user historical behaviors. However, privacy issue arises in this process as sensitive user private data are collected by the recommender server. Recently proposed privacy-preserving collaborative filtering (PPCF) methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in real online services. In this paper, an efficient privacy-preserving item-based collaborative filtering algorithm is proposed, which can protect user privacy during online recommendation process without compromising recommendation accuracy and efficiency. The proposed method is evaluated using the Netflix Prize dataset. Experimental results demonstrate that the proposed method outperforms a randomized perturbation based PPCF solution and a homomorphic encryption based PPCF solution by over 14X and 386X, respectively, in recommendation efficiency while achieving similar or even better recommendation accuracy.     

基于多维特征交叉的深度协同过滤算法

基于深度学习的推荐算法最初以用户和物品的ID信息作为输入,但是ID无法很好地表现用户与物品的特征。在原始数据中,用户对物品的评分数据在一定程度上能表现出用户和物品的特征,但是未考虑用户的评分偏好以及物品的热门程度。在评分任务中使用隐式反馈和ID信息作为用户与物品的特征,在消除用户主观性对特征造成的噪声的同时在一定程度上缓解冷启动问题,利用单层神经网络对原始高维稀疏特征降维,使用特征交叉得到用户与物品的低阶交互,再利用神经网络捕获用户与物品的高阶交互,有效提取了特征间的高低阶交互。在四个公开数据集上的实验表明,该算法能有效提高推荐精度。     

融合时间隐语义填充和子群划分的推荐算法

随着个性化推荐技术的发展，推荐系统面临着越来越多的挑战。传统的推荐算法通常存在数据稀疏性和推荐精度低等问题。针对以上问题，提出了一种融合时间隐语义填充和子群划分的推荐算法[K]-TLFM（Time Based Latent Factor Model Integrated with [k]-means）。该算法利用融合时间因素的隐语义模型对原始用户物品评分矩阵缺失项进行填充，避免了用全局平均值或者用户/物品平均值补全矩阵带来的误差，有效缓解了数据稀疏性问题，同时融合时间因素有效地刻画了用户偏好随时间的变化；完成评分矩阵缺失项填充后，基于二分[k]-means聚类算法将偏好、兴趣特征相似的对象划分到同一个子群中，在目标用户所属的子群中基于选定的协同过滤算法为用户产生推荐列表，提高了推荐效率和准确性。在MovieLens和Netflix数据集上对该算法的推荐性能进行了对比实验，结果表明该算法具有更高的推荐精度。     

Recommendation algorithm based on item quality and user rating preferences

Recommender systems are one of the most important technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filtering CF family, have problems such as scalability and sparseness. These problems hinder further developments of recommender systems. We propose a new recommendation algorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accuracy in rating prediction compared with the traditional approaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.     

基于项目分类和用户群体兴趣的协同过滤算法

由于用户评分数据在极端稀疏的情况下会导致传统协同过滤算法的推荐质量下降,针对该问题,提出一种基于项目分类和用户群体兴趣的协同过滤算法。该算法根据项目类别信息对项目进行分类,相同分类的项目具有较高的相似性;利用评分数据计算各个项目分类上的用户相似性矩阵,并计算用户群体在各个分类上的兴趣,通过二者构造加权的用户相似性矩阵;利用用户加权相似性矩阵寻找用户的最近邻以获得最佳的推荐效果。实验结果表明,该算法能有效提高推荐质量。     

基于深度自动编码器的托攻击集成检测方法

在采用协同过滤技术的推荐系统中,恶意用户通过注入大量虚假概貌使系统的推荐结果产生偏离,达到其攻击目的。为了检测托攻击,根据用户的评分值或基于攻击时间的集中性假设,从不同视角提取攻击概貌的特征。但是,这些基于人工特征的检测方法严重依赖于特征工程的质量,而且人工提取的检测特征多限于特定类型的攻击,提取特征也需要较高的知识成本。针对这些问题,从用户评分项目的时间偏好信息入手,提出一种利用深度稀疏自动编码器自动提取检测特征的托攻击集成检测方法。利用小波变换将项目在不同时间间隔内的流行度设定为多个等级,对用户的评分数据预处理得到用户-项目时间流行度等级矩阵。然后,采用深度稀疏自动编码器对用户-项目时间流行度等级矩阵自动进行特征提取,得到用户评分模式的低层特征表达,消除了传统的人工特征工程。以SVM作为基分类器,在深度稀疏自动编码器的每层提取特征并进行攻击检测,生成最终的集成检测结果。在Netflix数据集上的实验表明,提出的检测方法对均值攻击、AoP攻击、偏移攻击、高级项目攻击、高级用户攻击具有较好的检测效果。     

Exploiting interactions of review text,hidden user communities and item groups,and time for collaborative filtering

Rich side information concerning users and items are valuable for collaborative filtering (CF) algorithms for recommendation. For example, rating score is often associated with a piece of review text, which is capable of providing valuable information to reveal the reasons why a user gives a certain rating. Moreover, the underlying community and group relationship buried in users and items are potentially useful for CF. In this paper, we develop a new model to tackle the CF problem which predicts user’s ratings on previously unrated items by effectively exploiting interactions among review texts as well as the hidden user community and item group information. We call this model CMR (co-clustering collaborative filtering model with review text). Specifically, we employ the co-clustering technique to model the user community and item group, and each community–group pair corresponds to a co-cluster, which is characterized by a rating distribution in exponential family and a topic distribution. We have conducted extensive experiments on 22 real-world datasets, and our proposed model CMR outperforms the state-of-the-art latent factor models. Furthermore, both the user’s preference and item profile are drifting over time. Dynamic modeling the temporal changes in user’s preference and item profiles are desirable for improving a recommendation system. We extend CMR and propose an enhanced model called TCMR to consider time information and exploit the temporal interactions among review texts and co-clusters of user communities and item groups. In this TCMR model, each community–group co-cluster is characterized by an additional beta distribution for time modeling. To evaluate our TCMR model, we have conducted another set of experiments on 22 larger datasets with wider time span. Our proposed model TCMR performs better than CMR and the standard time-aware recommendation model on the rating score prediction tasks. We also investigate the temporal effect on the user–item co-clusters.     

基于注意力机制的协同卷积动态推荐网络

一直以来, 各种推荐系统关注于如何挖掘用户与物品特征间的潜在关联, 特征信息的充分利用有利于用户到物品的精准匹配. 基于矩阵分解和分解机的推荐算法是该领域的主流, 前者学习用户历史行为而后者分析对象特征关系, 但都难以兼顾用户行为与个体特征. 而近年来, 深度神经网络凭借其强大的特征学习能力和灵活可变的结构被应用到了推荐系统领域. 鉴于此, 本文提出了一种基于注意力机制的协同卷积动态推荐网络(Attentionbased collaborative convolutional dynamic network, ACCDN), 它通过注意力机制实现用户历史行为、用户画像与物品属性的多重交互, 再通过卷积网络逐层捕捉更高阶的特征交互. 网络同时接受不同组块输出的低阶至高阶信息, 最后给出用户对指定物品青睐评分概率的预估. 而且本文还提出了一种基于无参时间衰减的用户兴趣标签来量化用户关注的变化. 通过比较若干先进模型在两个现实数据集的表现, 本文设计的动态推荐模型不但能够缓解推荐时滞性, 还能明显提高推荐质量, 为用户带来更好的个性化服务体验.     

基于评分矩阵局部低秩假设的成列协同排名算法

协同过滤方法是当今大多数推荐系统的核心.传统的协同过滤方法专注于评分预测的准确性,然而实际推荐系统的推荐结果往往是项目的排序.针对这一问题,将排名学习领域的知识引入推荐算法,设计了一种基于评分矩阵局部低秩假设的成列协同排名算法.选择直接使用计算复杂度较低的成列损失函数来优化矩阵分解模型,并通过实验验证了其在运算速度上的显著提升.在3个实际推荐系统数据集上,与当下主流推荐算法的比较实验结果表明,该算法具有良好的性能.