Biblio

This paper presents a computational study of Thai legal documents using text mining and network analytic approach. Thai legal systems rely much on the existing judicial rulings. Thus, legal documents contain complex relationships and require careful examination. The objective of this study is to use text mining to model relationships between these legal documents and draw useful insights. A structure of document relationship was found as a result of the study in forms of a network that is related to the meaningful relations of legal documents. This can potentially be developed further into a document retrieval system based on how documents are related in the network.

Recommender systems (RSs) are intelligent systems to help e-commerce users to find their preferred items among millions of available items by considering the profiles of both users and items. These systems need to predict the unknown ratings and then recommend a set of high rated items. Among the others, Collaborative Filtering (CF) is a successful recommendation approach and has been utilized in many real-world systems. CF methods seek to predict missing ratings by considering the preferences of those users who are similar to the target user. A major task in Collaborative Filtering is to identify an accurate set of users and employing them in the rating prediction process. Most of the CF-based methods suffer from the cold-start issue which arising from an insufficient number of ratings in the prediction process. This is due to the fact that users only comment on a few items and thus CF methods faced with a sparse user-item matrix. To tackle this issue, a new collaborative filtering method is proposed that has a trust-aware strategy. The proposed method employs the trust relationships of users as additional information to help the CF tackle the cold-start issue. To this end, the proposed integrated trust relationships in the prediction process by using the Ant Colony Optimization (ACO). The proposed method has four main steps. The aim of the first step is ranking users based on their similarities to the target user. This step uses trust relationships and the available rating values in its process. Then in the second step, graph clustering methods are used to cluster the trust graph to group similar users. In the third step, the users are weighted based on their similarities to the target users. To this end, an ACO process is employed on the users' graph. Finally, those of top users with high similarity to the target user are used in the rating prediction process. The superiority of our method has been shown in the experimental results in comparison with well-known and state-of-the-art methods.