| Summary: | 碩士 === 國立交通大學 === 管理學院碩士在職專班資訊管理組 === 99 === The rising of Global Internet (World-Wide Web) changed the transactions of the traditional way, contributed to the rapid development of e-commerce, and maked the explosive growth of the online stores and products. However, if the online consumers want to find their wanted goods, they must spend considerable time and cost. That's why Personalized Recommendation System can help consumers to find the favorite goods by predicting and providing a recommended list of goods to consumers, to save lots of time and cost of their searching for information.
The main purpose of this study is using more complete factors, that is, similarity, trust value, and the relations of social network, to make the recommended mechanism to close to the actual assessment of consumers. We also try to improve the limitations and shortcomings of Collaborative Filtering Recommendation System.
Summarized by statistical analysis of the questionnaire, the results found that even facing the same product/category, consumers will have their own decision-making considerations, and significantly affected by the impact of personality traits, such as gender, age, economic ability. The experimental results also found that, if combining these factors and weights in the recommended system's mechanism, it will be a greater improvement for enhancing the accuracy of Recommended systems.
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