Fuzzy-genetic approach to recommender systems based on a novel hybrid user model

The main strengths of collaborative filtering (CF), the most successful and widely used filtering technique for recommender systems, are its cross-genre or ‘outside the box’ recommendation ability and that it is completely independent of any machine-readable representation of the items being recommended. However, CF suffers from sparsity, scalability, and loss of neighbor transitivity. CF techniques are either memory-based or model-based. While the former is more accurate, its scalability compared to model-based is poor. An important contribution of this paper is a hybrid fuzzy-genetic approach to recommender systems that retains the accuracy of memory-based CF and the scalability of model-based CF. Using hybrid features, a novel user model is built that helped in achieving significant reduction in system complexity, sparsity, and made the neighbor transitivity relationship hold. The user model is employed to find a set of like-minded users within which a memory-based search is carried out. This set is much smaller than the entire set, thus improving system’s scalability. Besides our proposed approaches are scalable and compact in size, computational results reveal that they outperform the classical approach.     

基于检测响应的安全协同推荐系统研究

协同推荐系统广泛地应用于电子商务和信息访问系统,为新用户提供个性化的产品建议。然而,协同推荐系统存在着严重的安全隐患,使得恶意用户能够注入伪造的描述文件,影响或破坏提供给其他用户的推荐建议。本文探讨了检测响应描述文件注入攻击的方法,改进了协同过滤推荐算法,设计了基于检测响应方法的安全协同推荐系统框架。     

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.     

An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance

In the present day, the oversaturation of data has complicated the process of finding information from a data source. Recommender systems aim to alleviate this problem in various domains by actively suggesting selective information to potential users based on their personal preferences. Amongst these approaches, collaborative filtering based recommenders (CF recommenders), which make use of users’ implicit and explicit ratings for items, are widely regarded as the most successful type of recommender system. However, CF recommenders are sensitive to issues caused by data sparsity, where users rate very few items, or items receive very few ratings from users, meaning there is not enough data to give a recommendation. The majority of studies have attempted to solve these issues by focusing on developing new algorithms within a single domain. Recently, cross-domain recommenders that use multiple domain datasets have attracted increasing attention amongst the research community. Cross-domain recommenders assume that users who express their preferences in one domain (called the target domain) will also express their preferences in another domain (called the source domain), and that these additional preferences will improve precision and recall of recommendations to the user. The purpose of this study is to investigate the effects of various data sparsity and data overlap issues on the performance of cross-domain CF recommenders, using various aggregation functions. In this study, several different cross-domain recommenders were created by collecting three datasets from three separate domains of a large Korean fashion company and combining them with different algorithms and different aggregation approaches. The cross-recommenders that used high performance, high overlap domains showed significant improvement of precision and recall of recommendation when the recommendation scores of individual domains were combined using the summation aggregation function. However, the cross-recommenders that used low performance, low overlap domains showed little or no performance improvement in all areas. This result implies that the use of cross-domain recommenders do not guarantee performance improvement, rather that it is necessary to consider relevant factors carefully to achieve performance improvement when using cross-domain recommenders.     

GRIP: A Group Recommender Based on Interactive Preference Model

Numerous applications of recommender systems can provide us a tool to understand users. A group recommender reflects the analysis of multiple users’ behavior, and aims to provide each user of the group with the things they involve according to users’ preferences. Currently, most of the existing group recommenders ignore the interaction among the users. However, in the course of group activities, the interactive preferences will dramatically affect the success of recommenders. The problem becomes even more challenging when some unknown preferences of users are partly influenced by other users in the group. An interaction-based method named GRIP (Group Recommender Based on Interactive Preference) is presented which can use group activity history information and recommender post-rating feedback mechanism to generate interactive preference parameters. To evaluate the performance of the proposed method, it is compared with traditional collaborative filtering on the MovieLens dataset. The results indicate the superiority of the GRIP recommender for multi-users regarding both validity and accuracy.     

Deep learning techniques for recommender systems based on collaborative filtering

In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as one of the most prominent strategies to effectively deal with large datasets and is capable of offering users interesting content in a recommendation fashion. Nevertheless, it is well-known CF recommenders suffer from data sparsity, mainly in cold-start scenarios, substantially reducing the quality of recommendations. In the vast literature about the aforementioned topic, there are numerous solutions, in which the state-of-the-art contributions are, in some sense, conditioned or associated with traditional CF methods such as Matrix Factorization (MF), that is, they rely on linear optimization procedures to model users and items into low-dimensional embeddings. To overcome the aforementioned challenges, there has been an increasing number of studies exploring deep learning techniques in the CF context for latent factor modelling. In this research, authors conduct a systematic review focusing on state-of-the-art literature on deep learning techniques applied in collaborative filtering recommendation, and also featuring primary studies related to mitigating the cold start problem. Additionally, authors considered the diverse non-linear modelling strategies to deal with rating data and side information, the combination of deep learning techniques with traditional CF-based linear methods, and an overview of the most used public datasets and evaluation metrics concerning CF scenarios.     

Preventing Recommendation Attack in Trust-Based Recommender Systems

Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that “victim” nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the “victim” nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.     

云模式用户行为关联聚类的协同过滤推荐算法

传统的协同过滤推荐算法基于互联网模式单纯从某个角度研究电子商务推荐问题,推荐质量明显不高。为改善推荐效果,提高推荐系统的伸缩性和实用价值,基于研究云模式的用户行为相似性度量公式、用户行为等级函数、关联规则函数,定义关联聚类方法,改进相应算法,提出一种云模式用户行为关联聚类的协同过滤推荐算法。最后使用MovieLens和阿里巴巴的云测试数据进行局部实验与全局实验,并对各种算法的实验结果进行对比分析。实验结果表明,该算法推荐效果明显优于传统算法,具有较强的伸缩性和较高的实用价值。     

A Fuzzy Recommender System for Public Library Catalogs

Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton‐area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative‐filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold‐start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.     

结合似然关系模型和用户等级的协同过滤推荐算法

针对传统协同过滤推荐算法的稀疏性、扩展性问题,提出了结合似然关系模型和用户等级的协同过滤推荐算法.首先,定义了用户等级函数,采用基于用户等级的协同过滤方法,在不影响推荐质量的前提下有效提高了推荐效率,从而解决扩展性问题;然后,将其与似然关系模型相结合,使之能够综合利用用户信息、项目信息、用户对项目的评分数据,对不同用户给出不同的推荐策略,从而解决稀疏性问题,提高推荐质量.在MovieLens数据集上的实验结果表明,该算法比单纯使用基于似然关系模型或传统协同过滤技术的推荐算法,不仅推荐质量有所提高,推荐速度比传统协同过滤算法明显加快.     

DCFLA: A distributed collaborative-filtering neighbor-locating algorithm

Although collaborative filtering (CF) has proved to be one of the most successful techniques in recommendation systems, it suffers from a lack of scalability as the time complexity rapidly increases when the number of the records in the user database increases. As a result, distributed collaborative filtering (DCF) is attracting increasing attention as an alternative implementation scheme for CF-based recommendation systems. In this paper, we first propose a distributed user-profile management scheme using distributed hash table (DHT)-based routing algorithms, which is one of the most popular and effective approaches in peer-to-peer (P2P) overlay networks. In this DCF scheme, an efficient DCF neighbor-locating algorithm (DCFLA) is proposed, together with two improvements, most same opinion (MSO) and average rating normalization (ARN), to reduce the network traffic and time cost. Finally, we analyze the performance of one baseline and three novel CF algorithms are being proposed: (1) a traditional memory-based CF (baseline); (2) a basic DHT-based CF; (3) a DHT-based CF with MSO; and (4) a DHT-based CF with MSO and ARN. The experimental results show that the scalability of our proposed DCFLA is much better than the traditional centralized CF algorithm and the prediction accuracies of these two systems are comparable.     

基于滚动时间窗的动态协同过滤推荐模型及算法

为了提高传统的协同过滤推荐系统的性能,首次提出了考虑时序性的基于滚动时间窗的用户一项目一时间三 维动态模型,并在此基础上研究了针对该模型的协同过滤推荐算法。该模型算法对不同时间的兴趣评分按时间序列 处理,用户兴趣相似度由不同时间段的分量组合而成,提高了算法的时效性;进而推导出了该模型的增量算法,利用增 量算法减少了计算相似度的时间复杂度,从而提高了算法的扩展性;最后设计了合理的实验,实验结果表明提出的三 维动态模型及算法在命中率性能上优于传统的二维协同过滤推荐模型及算法。     

基于谱聚类和扩展朴素贝叶斯的混合推荐算法

随着电子商务的发展,基于协同过滤的推荐算法越来越受欢迎,与此同时,该算法的缺陷也越来越明显,如数据稀疏性、系统可扩展性等。为此,提出一种混合型推荐算法。该混合算法首先利用谱聚类方法,根据图谱理论将聚类问题转化为图的分割问题,寻找相似数据群;然后,利用扩展逻辑回归的朴素贝叶斯算法对聚类结果建立预测模型;最后使用增量式更新的方法,在不全部重新训练模型的基础上,对模型进行局部修改。实验结果表明,该算法较传统的协同过滤算法在一定程度上克服了数据稀疏性和冷启动问题,降低了计算复杂度,并且具有更好的准确性和可扩展性。     

Identifying the effects of SVD and demographic data use on generalized collaborative filtering

The purpose of this paper is to examine how singular value decomposition (SVD) and demographic information can improve the performance of plain collaborative filtering (CF) algorithms. After a brief introduction to SVD, where the method is explained and some of its applications in recommender systems are detailed, we focus on the proposed technique. Our approach applies SVD in different stages of an algorithm, which can be described as CF enhanced by demographic data. The results of a rather long series of experiments, where the proposed algorithm is successfully blended with user- and item-based CF, show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of recommender systems, but also assists in increasing the accuracy of systems employing it.     

Improving neighborhood based Collaborative Filtering via integrated folksonomy information

Personalized recommender systems which can provide people with suggestions according to individual interests usually rely on Collaborative Filtering (CF). The neighborhood based model (NBM) is a common choice when implementing such recommenders due to the intuitive nature; however, the recommendation accuracy is a major concern. Current NBM based recommenders mostly address the accuracy issue based on the rating data alone, whereas research on hybrid recommender systems suggests that users enjoy specifying feedback about items across multiple dimensions. In this work we aim to improve the accuracy of NBM via integrating the folksonomy information. To achieve this objective, we first propose the folksonomy network (FN) to analyze the item relevance described by the folksonomy data. We subsequently integrate the obtained folksonomy information into the global-optimization based NBM for making multi-source based recommendations. Experiments on the MovieLens dataset suggest positive results, which prove the efficiency of our strategy.     

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.     

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.     

A Web service QoS prediction approach based on time- and location-aware collaborative filtering

In QoS-based Web service recommendation, predicting quality of service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. CF algorithms can be divided into two main categories: memory-based and model-based algorithms. Memory-based CF algorithms are easy to implement and highly effective, but they suffer from a fundamental problem: inability to scale-up. Model-based CF algorithms, such as clustering CF algorithms, address the scalability problem by seeking users for recommendation within smaller and highly similar clusters, rather than within the entire database. However, they are often time-consuming to build and update. In this paper, we propose a time-aware and location-aware CF algorithms. To validate our algorithm, this paper conducts series of large-scale experiments based on a real-world Web service QoS data set. Experimental results show that our approach is capable of addressing the three important challenges of recommender systems–high quality of prediction, high scalability, and easy to build and update.     

Probabilistic memory-based collaborative filtering

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper, we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the "new user problem." Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real-world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.