Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

& Sciences Publication Pvt. Ltd. Review on Various Kinds of Die Less Forming Methods

Abstract—With the increasing demands for low-volume and customer-made products, a die-less forming method, also called Incremental Sheet Metal Forming (ISMF), has become one of the leading research and development topics in the industry. Incremental Sheet Metal Forming (ISMF) is a recently invented die-less forming method that is quite different to the traditional methods. In ISMF, a piece of sheet metal is formed to the desired shape by a series of small incremental deformations. As it does not use dies, ISMF is effective for small batch production and prototypes. There are various kinds of die-less forming methods which can produce sheet metal parts without dies are proposed. This paper can help anyone who is interested in Incremental Sheet Metal forming with insight for future research direction. Index Terms — Die-less forming, Incremental sheet metal forming, Sheet metal parts

An empirical study into model testability

Testability modeling has been performed for many years. Unfortunately, the modeling of a design for testability is often performed after the design is complete. This limits the functional use of the testability model to determining what level of test coverage is available in the design. This information may be useful to help assess whether a product meets a requirement to achieve a desired level of test coverage, but has little pro-active effect on making the design more testable. This paper investigates and presents a number of approaches for tackling this problem. Approaches are surveyed, achievements and main issues of each approach are considered. Investigation of that classification will help researchers who are working on model testability to deliver more applicable solutions

A framework for evaluation of enterprise architecture implementation methodologies

Enterprise Architecture (EA) Implementation Methodologies have become an important part of EA projects. Several implementation methodologies have been proposed, as a theoretical and practical approach, to facilitate and support the development of EA within an enterprise. A significant question when facing the starting of EA implementation is deciding which methodology to utilize. In order to answer this question, a framework with several criteria is applied in this paper for the comparative analysis of existing EA implementation methodologies. Five EA implementation methodologies including: EAP, TOGAF, DODAF, Gartner, and FEA are selected in order to compare with proposed framework. The results of the comparison indicate that those methodologies have not reached a sufficient maturity as whole due to lack of consideration on requirement management, maintenance, continuum, and complexities in their process. The framework has also ability for the evaluation of any kind of EA implementation methodologies