Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. © 2014 Ding et al

Hierarchical graph maps for visualization of collaborative recommender systems

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system?s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system?s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies

A Broad Learning Approach for Context-Aware Mobile Application Recommendation

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method

Knowledge discovery in database: A knowledge management strategic approach

Knowledge management had been a critical focus and interest in Information Technology, especially as it affects business organizations through the implementation of business intelligence and expertise.Knowledge discovery and knowledge conversion (tacit/implicit to explicit knowledge) play important roles in these aspects; through the application of technologies in the SECI model to aid knowledge management, and identifying the sources of the expertise whether in humans or physical databases serve as the basis for expertise’s knowledge management.This paper presents in detail the significances of knowledge discovery in databases (KDD) in achieving an all encompassing knowledge management strategy.This strategy must comprise of transparent and multiple interrelationships of organizational agents through shared mental maps, collaborative and distributed technologies, and solves all problem in other ways with a special focus on data mining which is also found in the KDD process. Extensive literatures were reviewed to operationalize Knowledge discovery in human and in data ware houses as its affect knowledge management, and bring to the fore the processes involved in KDD process, its applications, understanding using SECI model, possible challenges, and suggest the future research areas to solve the observed challenges

Information-Based Neighborhood Modeling

Since the inception of the World Wide Web, the amount of data present on websites and internet infrastructure has grown exponentially that researchers continuously develop new and more efficient ways of sorting and presenting information to end-users. Particular websites, such as e-commerce websites, filter data with the help of recommender systems. Over the years, methods have been developed to improve recommender accuracy, yet developers face a problem when new items or users enter the system. With little to no information on user or item preferences, recommender systems struggle generating accurate predictions. This is the cold-start problem. Ackoff defines information as data structured around answers to the question words: what, where, when, who and how many. This paper explores how Ackoff’s definition of information might improve accuracy and alleviate cold-start conditions when applied to the neighborhood model of collaborative filtering (Ackoff, 1989, p. 3)

Social and content hybrid image recommender system for mobile social networks

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user