A recommender system aims to provide users with personalized online product or service recommendations to handle the online information overload problem that keep rapidly increasing. The main problems in order to resolve the problems, one of the current trust aware mechanism that includes rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the performances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the tw Keywords: recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly increasing. The main problems in the CF recommender system are sparsity and cold start. In order to resolve the problems, one of the current researches has been directed to the CF with echanism that includes trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the different views have an effect on the accuracy and rating coverage of the two algorithms.recommender system; collaborative filtering; trust aware; distrust. A recommender system aims to provide users with personalized online product or service e online information overload problem that keep rapidly CF recommender system are sparsity and cold start. In been directed to the CF with a trust as additional information in order to predict the rating for sparse data. This paper provides a review of the existing recommender system implementing the CF and trust aware. Furthermore, based on an empirical experiment, the erformances of two recommender system approaches with trust aware and distrust in different views of trusted users are also reported in this paper. The results have shown that the o algorithms.

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In this paper, we are introducing a new method of granular exam-to-slot allocation based on the preprocessing of the basic student-exam information into a more abstract (granulated) entity of conflict chains. Since the conflict chains are designed to capture the mutual dependencies between exams, they enable us to reason about the exam-to-slot allocation for all exams in a chain rather than just one exam at-a-time. The initial exam-to-slot allocation, generated through the processing of conflict chains, is then refined by considering the spread of the exams in the examination session so as to minimize the appropriately defined cost function. The granular pre-processing of problem data has been shown to enhance the efficiency of the exam scheduling task and has led to the identification of very competitive exam schedules.

This research aims to propose TOPSIS model in evaluating, comparing, and ranking the Malaysian companies under the construction sector according to their financial performance using financial ratio. Previously, TOPSIS model and financial ratio as tools of measurement have been applied to evaluate, compare, and rank the financial performance of the companies. Nevertheless, there are not many researches have been done on TOPSIS model in Malaysia’s construction sector. Hence, this paper intends to fill in the loopholes by assessing the performance of the companies from the construction sector in Malaysia stock market by implementing the proposed model. Based from the findings, TOPSIS model was able to assess the companies’ financial performance and consequently rank them. The results from the study parallel with the study done by other investment agencies. Therefore, this method can be opted to substitute the fundamental and technical valuation that is commonly utilised by the investment analysts.

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