Collaborative filtering recommendation system : a framework in massive open online courses

Massive open online courses (MOOCs) are growing relatively rapidly in the education environment. There is a need for MOOCs to move away from its one-size-fit-all mode. This framework will introduce an algorithm based recommendation system, which will use a collaborative filtering method (CFM). Collaborative filtering method (CFM) is the process of evaluating several items through the rating choices of the participants. Recommendation system is widely becoming popular in online study activities; we want to investigate its support to learning and encouragement to more effective participation. This research will be reviewing existing literature on recommender systems for online learning and its support to learners’ experiences. Our proposed recommendation system will be based on course components rating. The idea was for learners to rate the course and components they have studied in the platform between the scales of 1 – 5. After the rating, we then extract the values into a comma separated values (CSV) file then implement recommendation using Python programming based on learners with similar rating patterns. The aim was to recommend courses to different users in a text editor mode using Python programming. Collaborative filtering will act upon similar rating patterns to recommend courses to different learners, so as to enhance their learning experience and enthusiasm

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm

To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on human-computer interaction (HCI) design and recommender system

A Network Science and Document Similarity based Hybrid Job Recommendation System

Tööde soovitussüsteemid kasutavad erinevaid andmeallikaid lõppkasutajale parema sisu tagamiseks. Hästi toimiva soovitussüsteemi arendamine nõuab keerulisi hübriidseid lähenemisi sarnasuse kujutamisele põhinedes töökuulutuste ja resümeede sisudele ja nendevahelistele interaktsioonidele. Antud töö tulemina arendati efektiivne võrgul baseeruv töökohtade soovitussüsteem, mis kasutab Personalized PageRank algoritmi töökohtade järjestamiseks põhinedes tööotsija resümee ja töökuulutuse kui tekstiliste dokumentide sarnasustele ning eelnevatele kasutaja ja töökuulutuste vahelistele interaktsioonidele.Meie lähenemine saavutas 50%-lise saagise ja tekitas online A/B testi jooksul rohkem kandideerimisi kui eelmised algoritmid.Job recommendation systems mainly use different sources of data in order to give the better content for the end user. Developing the well-performing system requires complex hybrid approaches of representing similarity based on the content of job postings and resumes as well as interactions between them. We develop an efficient hybrid network-based job recommendation system which uses Personalized PageRank algorithm in order to rank vacancies for the users based on the similarity between resumes and job posts as textual documents, along with previous interactions of users with vacancies. Our approach achieved the recall of 50% and generated more applies for the jobs during the online A/B test than previous algorithms

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Using automatic speech transcriptions in lecture recommendation systems

One problem created by the success of video lecture repositories is the difficulty faced by individual users when choosing the most suitable video for their learning needs from among the vast numbers available on a given site. Recommender systems have become extremely common in recent years and are used in many areas. In the particular case of video lectures, automatic speech transcriptions can be used to zoom in on user interests at a semantic level, thereby improving the quality of the recommendations made. In this paper, we describe a video lecture recommender system that uses automatic speech transcriptions, alongside other relevant text resources, to generate semantic lecture and user models. In addition, we present a real-life implementation of this system for the VideoLectures.NET repository.The research leading to these results has received funding from the PASCAL2 Network of Excellence under the PASCAL Harvest Project La Vie, the EU 7th Framework Programme (FP7/2007-2013) under grant agreement no. 287755 (transLectures), the ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no. 621030 (EMMA), the Spanish MINECO Active2Trans (TIN2012-31723) research project, and by the Spanish Government with the FPU scholarship AP2010-4349.Pérez González De Martos, AM.; Silvestre Cerdà, JA.; Rihtar, M.; Juan Císcar, A.; Civera Saiz, J. (2014). Using automatic speech transcriptions in lecture recommendation systems. En Conference Proceedings iberSPEECH 2014 : VIII Jornadas en Tecnologías del Habla and IV SLTech Workshop. Universidad de Las Palmas de Gran Canaria. 149-158. http://hdl.handle.net/10251/54395S14915