Content curation algorithm on blog posts using hybrid computing

Multimedia Tools and Applications - Content curation is a significant step to identify the relevant content for the searched topics. There are many methods introduced to generate summarized...     

Personalized recommendation of popular blog articles for mobile applications

Weblogs have emerged as a new communication and publication medium on the Internet for diffusing the latest useful information. Providing value-added mobile services, such as blog articles, is increasingly important to attract mobile users to mobile commerce, in order to benefit from the proliferation and convenience of using mobile devices to receive information any time and anywhere. However, there are a tremendous number of blog articles, and mobile users generally have difficulty in browsing weblogs owing to the limitations of mobile devices. Accordingly, providing mobile users with blog articles that suit their particular interests is an important issue. Very little research, however, has focused on this issue.In this work, we propose a novel Customized Content Service on a mobile device (m-CCS) to filter and push blog articles to mobile users. The m-CCS includes a novel forecasting approach to predict the latest popular blog topics based on the trend of time-sensitive popularity of weblogs. Mobile users may, however, have different interests regarding the latest popular blog topics. Thus, the m-CCS further analyzes the mobile users’ browsing logs to determine their interests, which are then combined with the latest popular blog topics to derive their preferred blog topics and articles. A novel hybrid approach is proposed to recommend blog articles by integrating personalized popularity of topic clusters, item-based collaborative filtering (CF) and attention degree (click times) of blog articles. The experiment result demonstrates that the m-CCS system can effectively recommend mobile users’ desired blog articles with respect to both popularity and personal interests.     

NP-miner: A real-time recommendation algorithm by using web usage mining

Web usage mining is widely applied in various areas, and dynamic recommendation is one web usage mining application. However, most of the current recommendation mechanisms need to generate all association rules before recommendations. This takes lots of time in offline computation, and cannot provide real-time recommendations for online users. This study proposes a Navigational Pattern Tree structure for storing the web accessing information. Besides, the Navigational Pattern Tree supports incremental growth for immediately modeling web usage behavior. To provide real-time recommendations efficiently, we develop a Navigational Pattern mining (NP-miner) algorithm for discovering frequent sequential patterns on the proposed Navigational Pattern Tree. According to historical patterns, the NP-miner scans relevant sub-trees of the Navigational Pattern Tree repeatedly for generating candidate recommendations. The experiments study the performance of the NP-miner algorithm through synthetic datasets from real applications. The results show that the NP-miner algorithm can efficiently perform online dynamic recommendation in a stable manner.     

A new slope one based recommendation algorithm using virtual predictive items

Although the Slope One family of algorithms provides an appealing solution to the scalability problem in collaborative filtering recommendation systems, the data sparsity problem as a major issue still remains open. Many of the recent algorithms rely on sophisticated methods which not only have negative effect on the scalability of Slope One, but also need some additional information extra to ratings matrix. To address these problems in this paper, we have proposed a novel method based on Weighted Slope One algorithm which introduces virtual predictive items in relatively sparse ratings databases. These virtual items are those which neither have rated by active users nor have deviation to active items. The strength of our approach lies in its ability to manage the data sparsity problem without using any extra information. Indeed, it uses the ratings data which are common in collaborative filtering systems. Our proposed algorithm is scalable, easy to implement and updatable on the fly (without changing comprehensively). Experimental results on the MovieLens and Netflix datasets show the effectiveness of the proposed algorithm in handling data sparsity problem. It also outperforms some state-of-the-art collaborative filtering algorithms in terms of prediction quality.     

A content-based recommendation algorithm for learning resources

Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L₁-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.     

A parallel algorithm for generating combinations

A parallel algorithm for generating all combinations of m items out of n given items in lexicographic order is presented. The computational model is a linear systolic array consisting of m identical processing elements. It takes (_mⁿ) time-steps to generate all the (_mⁿ) combinations. Since any processing element is identical and executes the same procedure, it is suitable for VLSI implementation. Based on mathematical induction, such algorithm is proved to be correct.     

A polynomial modeling based algorithm in top-N recommendation

Recommendation is the process of identifying and recommending items that are more likely to be of interest to a user. Recommender systems have been applied in variety of fields including e-commerce web pages to increase the sales through the page by making relevant recommendations to users. In this paper, we pose the problem of recommendation as an interpolation problem, which is not a trivial task due to the high dimensional structure of the data. Therefore, we deal with the issue of high dimension by representing the data with lower dimensions using High Dimensional Model Representation (HDMR) based algorithm. We combine this algorithm with the collaborative filtering philosophy to make recommendations using an analytical structure as the data model based on the purchase history matrix of the customers. The proposed approach is able to make a recommendation score for each item that have not been purchased by a customer which potentiates the power of the classical recommendations. Rather than using benchmark data sets for experimental assessments, we apply the proposed approach to a novel industrial data set obtained from an e-commerce web page from apparels domain to present its potential as a recommendation system. We test the accuracy of our recommender system with several pioneering methods in the literature. The experimental results demonstrate that the proposed approach makes recommendations that are of interest to users and shows better accuracy compared to state-of-the-art methods.     

一种基于随机游走的歌曲推荐算法

推荐系统可以帮助人们在海量的数据中发现所需的有价值的信息。传统的协同过滤推荐算法根据历史数据中用户对项目的各种行为操作构建用户-项目评分矩阵,进而计算相似度,从而预测用户对项目的偏好程度进行推荐。但因为评分数据通常较为稀疏,使得推荐的准确性不高,从而不能很好地对用户进行推荐。针对这个问题,提出一种结合场论理论的随机游走歌曲推荐算法,融合歌曲评分相似度和歌曲基本信息相似度,降低歌曲间综合相似度矩阵的稀疏性,并将物理学中的场论理论和歌曲的重要度结合,构造转移概率矩阵,从而实现歌曲推荐。实验表明,该算法较协同过滤算法的推荐准确性更佳。     

A movie recommendation algorithm based on genre correlations

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations.     

A synthetical approach for blog recommendation: Combining trust,social relation,and semantic analysis

Weblog is a good paradigm of online social network which constitutes web-based regularly updated journals with reverse chronological sequences of dated entries, usually with blogrolls on the sidebars, allowing bloggers link to favorite site which they are frequently visited. In this study we propose a blog recommendation mechanism that combines trust model, social relation and semantic analysis and illustrates how it can be applied to a prestigious online blogging system – wretch in Taiwan. By the results of experimental study, we found a number of implications from the Weblog network and several important theories in domain of social networking were empirically justified. The experimental evaluation reveals that the proposed recommendation mechanism is quite feasible and promising.     

A new algorithm for generating hilbert curves

A new algorithm is described which generates the co-ordinates of the tth point along a Hilbert curve, given the value of the parameter t. The algorithm is expressed in the concurrent programming language occam.     

A hybrid recommendation algorithm adapted in e-learning environments

With the rapid development of online learning technology, a huge amount of e-learning materials have been generated which are highly heterogeneous and in various media formats. Besides, e-learning environments are highly dynamic with the ever increasing number of learning resources that are naturally distributed over the network. On the other hand, in the online learning scenario, it is very difficult for users without sufficient background knowledge to choose suitable resources for their learning. In this paper, a hybrid recommender system is proposed to recommend learning items in users’ learning processes. The proposed method consists of two steps: (1) discovering content-related item sets using item-based collaborative filtering (CF), and (2) applying the item sets to sequential pattern mining (SPM) algorithm to filter items according to common learning sequences. The two approaches are combined to recommend potentially useful learning items to guide users in their current learning processes. We also apply the proposed approach to a peer-to-peer learning environment for resource pre-fetching where a central directory of learning items is not available. Experiments are conducted in a centralized and a P2P online learning systems for the evaluation of the proposed method and the results show good performance of it.     

基于链接和萤火虫算法聚类博文发现热点话题

针对已有的基于链接分析的热点发现方法存在准确度较低、易受作弊链接影响、易产生主题漂移现象等问题,利用复杂网络簇结构具有高度主题相关的特点,提出一种融合应用链接分析和萤火虫算法聚类博文的热点话题发现算法。以博文页面为节点,与博文内容相同或相关的链接作为边,根据博文及博主的相关属性,综合评定页面权重,建立博客话题模型;运用萤火虫算法对博文进行聚类获得聚类中心,按页面权重将聚类中心从大到小排序,形成热点话题热度排行。实验结果表明,该方法能够发现精度更高、数量更多的博客热点话题。     

协同过滤推荐算法

针对目前协同过滤推荐精度受损,且出现冷启动的问题,提出一种经过改进的协同过滤推荐算法。其主要思想是针对两种不同情况的目标项目采用不同的相似性计算方法。一种项目为新项目,分别通过信息熵法和项目属性相似性计算项目评分,然后通过平衡因子实现新项目评分的组合;另一种项目为非新项目,通过权重因子动态组合项目的属性相似性和评分相似性,获得最近邻居的评分推荐。实验结果表明,该算法能提高推荐算法的稳定性和精确度,同时解决冷启动问题。     

A simple algorithm for generating neuronal dendritic trees

A simple, efficient algorithm is presented for generating the codewords of all neuronal dendritic trees with a given number of terminal nodes. Furthermore, a procedure is developed for deciding if different codewords respond to topologically equivalent trees.     

A real-time recommendation engine using lambda architecture

In a data science theory, the recommended methodology is one of the most popular theories and has been deployed in many real industries. However, one of the most challenging problems these days is how to recommend items with massively streaming data. Therefore, this paper aims to do a real-time recommendation engine using the Lambda architecture. The Apache Hadoop and Apache Spark frameworks were used in this research to process the MovieLens dataset comprised 100 K and 20 M ratings from the GroupLens research. Using alternating least squares (ALS) and k-means algorithms, the top K recommendation movies and the top K trending movies for each user were shown as results. Additionally, the mean squared error (MSE) and within cluster sum of squared error (WCSS) had been computed to evaluate the performance of the ALS and k-means algorithms, sequentially. The results showed that they are acceptable since the MSE and WCSS values are low when comparing to the size of data. However, they can still be improved by tuning some parameters.     

A parallel SP-DBSCAN algorithm on spark for waiting spot recommendation

Multimedia Tools and Applications - It is challenging for complex urban transportation networks to recommend taxi waiting spots for mobile passengers because the traditional centralized mining...