Quantitative analysis of Matthew effect and sparsity problem of recommender systems

Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders

Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies

Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating

Electronic government procurement adoption behavior amongst Malaysian SMEs

The aim of this study is to investigate the relationship between a model of electronic procurement (e-procurement) adoption behavior and the level of Government e-procurement adoption amongst Small Medium Enterprise (SME) in Malaysia. Data was collected through questionnaires that were distributed to SME selected randomly in all SME in Malaysia.The data were analyzed using factor analysis, reliability analysis, independent-sample t-test, descriptive statistics, Pearson Correlation and multiple regressions. Regression results reveals that ‘power’, ‘trust’ and ‘value’ have a positive relationship with the level of e-procurement adoption amongst SME in Malaysia.All dimensions, namely; the power of supplier, power of procurement, trust on supplier, trust on information technology, value of implementation system efficiency and value of cost efficiency were also correlated with the level of e-procurement adoption amongst SME. Past studies on e-procurement are beset by problems of buyer-seller relationship perspective.In addition, these studies are skewed towards Government-SME relationship perspective which the Government possesses more power than SME and provide a better incentive to educate and influence SME to adopt e-procurement.In investigation the relationship between a model of e-procurement adoption behavior and the level of Government e-procurement adoption amongst SME in Malaysia, this study also tries to provides recommendation to Malaysian government for improving the level of e-procurement adoption amongst SME

A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

Learning Style Inventory System: A Study on Improving Programming Language Subject

Learning Style Inventory System is developed to computerize Learning Style Model which indicates the learning style of each person through a set of question. Concurrently, the system will analyze the result and give recommendation to fit one learning style towards learning a programming subject. The output of the system will help students be more responsible of their studies, thus doing well with all their curricular activities as well as co-curricular activities. The main objective of this project is to study various style of learning and come up with a good solution to improve learning skill in order to increases UTP student's quality of examination result focusing in programming subject. The development of the project consists of five main phases which are Planning, Analysis, Design, Development and Implementation. Development phase is divided into two main parts. The first part is system development while the second part is system integration. For the data collection, a survey is conducted through a distribution of questionnaire to get student's feedback, reviewing articles and research done by some intellectual. With this, the author would like to concludethat this project meets its objective and will spread the awareness of Learning Style Preferences among UTP student and concurrently guide students to excel in programming subject

A Study on Energy Efficiency ofUTP Academic Buildings

A lot of energy is used annually by UTP office building to cater the UTP staff and ensure their comfort of working. The energy is mostly used to fulfill the requirement and basic needs of the staff such as lighting, thermal comfort, and office plug loads. Located in Malaysia, which has hot and humid climate causes the building to receive unnecessary solar radiation which causes heat gain. Due to that, improvements need to be made towards current condition of UTP building towards a more energy efficient and environment friendly. The objective of the project is to evaluate how far current UTP office buildings fulfill the building criteria that are prescribed by the requirements in Code of Practice on Energy Efficiency and Use of Renewable Energy for Non-Residential Building (MS 1525: 2007). Besides, it also aims to provide recommendations that could be implemented to reduce energy consumption through effective practices of efficient lighting and managing office plug loads. The study is carried out by incorporating building energy survey together with lighting system survey and office plug loads survey followed by data gathering and analysis. The results obtained shows building energy performance index of UTP is higher than the value set by MS 1525 guideline. UTP lighting system is found inefficient due to high level intensity and illuminance produced by the lights. For office plug loads, personal computer together with monitors results in the highest amount of energy consumption annually. New recommendations are suggested in which could further improve the energy efficiency of UTP office building. The recommendation for building envelope is to reduce the heat transfer coefficient (U-value) which will reduce the heating up of the office building. The recommendation for lighting system in the other hand is by using more day lighting which will allow the reduction of operating hours of the artificial lighting. For office plug loads, it is recommended that better power management is implemented and more efficient equipment should be used to replace the existing ones