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

Usability of Social Tags in Digital Libraries for E-Learning Environment

This study contributes to the academic literature concerning social tag systems for digital libraries, addressing the identified information gap from the user’s perspective. It defines social tagging tools and tests users’ perceptions about possible practices. Moreover, it evaluates the effect when using social tagging systems in digital libraries, to assess whether such a system enhances the search process, and to identify whether there is any significant relationship between using social tagging systems in digital libraries and user satisfaction. Although developments in the field of social tags have been significant in recent years, there remains an open question regarding their usability, particularly in the context of digital libraries. Therefore, there is a need for further investigation, exploration and evaluation, and so this work contributed to this by exploring the usability of social tagging in digital libraries in terms of accuracy for research, user satisfaction and adoptability. For this study, Saudi students were given the opportunity to use the system in the United Kingdom, and their experiences, and opinions regarding ease of use and adoptability were then analysed to determine if they would assist digital libraries in Saudi Arabia to achieve their educational goals and to ensure user numbers would not decrease. A quantitative approach and a qualitative approach were combined to collect and analyse the data used in this research. The two approaches were conducted in sequential phases. In the first quantitative phase, assessment measures were administrated to Saudi students using library websites while studying in the UK. Data was collected from 175 participants, and statistical analysis was conducted using SPSS. Cross tabulation was also used to describe the numerical data and a chi-square analysis was conducted to determine the relationship between the various study variables. In the follow-up qualitative phase, semi-structured interviews were undertaken with 15 Saudi students, to explore the proposed hypothesis in depth. This data was then thematically analysed. Results concerning the usability of social tagging in digital libraries obtained in western universities cannot be generalised to Saudi Arabian universities, because the context of Saudi Arabia differs culturally and academically (Alsurehi & Al Youbi, 1014). To address this, the study utilised a sample of Saudi Arabian students, who had had the opportunity to experience using social tags while studying abroad, specifically in the United Kingdom. Their experience might potentially be very important and this research could be considered a first attempt to examine the usability of social tags in digital libraries. Since to date few empirical studies have directly addressed the usability issues raised here in Saudi Arabia, this research also offers a contribution in this area. In addition, although this study relates to the Saudi perspective, the findings can also be considered valuable to Arab countries sharing similar cultural and academic traditions