Customized information service is very important for service provider nowadays. Traditional selective dissemination, as widely discussed in library community requires users¡¦ involvement and only serves a limited amount of users. In this thesis, we propose to employ data mining techniques to discover knowledge in circulation databases so as to provide customized service in library new book recommendation. Our research¡¦s data source is from National Sun Yat-Sen University¡¦s library. We follow a standard data mining procedure and report our experience in this thesis. Our research uses patron concept hierarchy and book hierarchy with given support threshold and confidence threshold to derived association rules with patron types being antecedent and book types being subsequent. Four algorithms, namely SBSP, SBMP, LatSBMP, MBMP are proposed to facilitate patron and book hierarchy search. Their complexities are compared analytically.

Electronic Newspapers has become one sort of important communication medium with the rapid growth of audiences on Internet. In Taiwan, the majority of famous news media have their own Electronic Newspapers, which provide a large amount of online news. Though audiences can receive a mass of news, it is difficult for them to find the news in which they are really interested within limited time. Therefore, this current study focused on the research of the mechanism for filtering and recommending news based on the Use and Gratification Theory. The purposes were to develop a personalized news recommendation method for Electronic Newspapers and to examine the effect of the recommendation methods. The results of the empirical study showed that the system with recommendation mechanism was superior to the one with none in both usage performance and overall satisfaction. In this study, there were two recommendation mechanisms. One of them is time-based mechanism which refers to derive the interest automatically by analyzing how much time the audiences spent on reading news, the other is feedback-based mechanism which refers to derive the interest by asking audiences explicitly. It was found that there were no significant differences between the two mechanisms in either usage performance or overall satisfaction. Therefore, the time-based mechanism is helpful for audiences in finding personal news.

Abstract Libraries have long been widely recognized as import information-offering institutes. Thousands of new books are acquired per month by our university¡Xa mid-sized university in Taiwan), and patrons may have difficulties identifying the small set of books that really interest them. This gives rise to the problem of finding an effective way to recommend patrons the newly arrived books in a library. In this work, we address this problem in finding generalized association rules between patrons and books. We first discuss how to identify relevant but independent patron attributes in regard of the books they checked out. Then, we propose a set of algorithms for generating large itemsets and evaluate their performance experimentally. In addition, we define interestingness of rules and propose an algorithm for pruning uninteresting rules. Finally, we apply our approach to the circulation data of National SUN Yat-Sen University library and report our experiences.

Literature digital libraries are the source of digitalized literature data, from which Researchers can search for articles that meet their personal interest. However, Users often confused by the large number of articles stored in a digital library and a single query will typically yield a large number of articles, among which only a small subset will indeed interest the user. To provide more effective and efficient information search, many systems are equipped with a recommendation subsystem that recommends articles that users might be interested. In this thesis, we aim to research a number of recommendation techniques for making personalized recommendation. In light of the previous work that used collaborative approach for making recommendation for literature digital libraries, in this thesis, we first propose three content-based recommendation approaches, followed by a set of hybrid approaches that combine both content-based and collaborative methods. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found the hybrid approaches yields better quality of articles recommendation.

The thesis examines sequential mining approaches in the context of treatment recommendation for Gliblastoma (GBM) patients. GBM is the most lethal and biologically the most aggressive forms of brain tumor with median survival of approximately 1 year. A significant challenge in treating such rare forms of cancer is to make the best decision about optimal treatment plans for patients after standard of care. We tailor the existing sequential mining approaches by adding constraints to mine significant treatment options for cancer patients. The goal of the work is to analyze which treatment patterns play a role in prolonging the survival period of patients. In addition to the treatment analysis, we also discover some interesting clinical and genomic factors, which influence the survival period of patients. A treatment advisor tool has been developed based on the predictive features discovered. This tool is used to recommend treatment guidelines for a new patient based on the treatments meted out to other patients sharing clinical similarity with the new patient. The recommendations are also guided by the influential treatment patterns discovered in the study. The tool is based on the notion of patient similarity and uses a weighted function to calculate the same. The recommendations made by the tool may influence the clinicians to have the patients record some vital data on their own. With the progression of the treatment the clinicians may want to add to or modify some of the vital data elements previously decided to be recorded. In such a case a static database would not be very efficient to record the data since manual intervention is inevitable to incorporate the changes in the database structure. To solve this problem we have developed a dynamic database evolution framework, which uses a form based interface to interact with the clinician to add or modify the data elements in a database. The clinicians are flexible to create a new form for patients or modify existing forms based on a patient’s condition. As a result, appropriate schema modifications would be done in the relational database at the backend to incorporate these changes maintaining relational consistency.

This report presents a survey of the state-of-the-art methods for building recommendation systems. The report mainly concentrates on systems that use the available side information in addition to a fraction of known affinity values such as ratings. Such data is referred to as Dyadic Data with Covariates (DyadC). The sources of side information being considered includes user/item entity attributes, temporal information and social network attributes. Further, two new models for recommendation systems that make use of the available side information within the collaborative filtering (CF) framework, are proposed. Review Quality Aware Collaborative Filtering, uses external side information, especially review text to evaluate the quality of available ratings. These quality scores are then incorporated into probabilistic matrix factorization (PMF) to develop a weighted PMF model for recommendation. The second model, Mixed Membership Bayesian Affinity Estimation (MMBAE), is based on the paradigm of Simultaneous Decomposition and Prediction (SDaP). This model simultaneously learns mixed membership cluster assignments for users and items along with a predictive model for rating prediction within each co-cluster. Experimental evaluation on benchmark datasets are provided for these two models. / text

Στην παρούσα διπλωματική εργασία προτείνουμε και υλοποιούμε ένα σύστημα, το οποίο κινείται στα πλαίσια των τεχνολογιών κινητού υπολογισμού και σχεδιάστηκε για να χρησιμοποιείται από τους καταναλωτές κατά την διαδικασία αγοράς αγαθών από τα ράφια των super market. / In this postgraduate project a proposed system is built within mobile computing technology and is designed for use from consumers while they go for shopping to the super market.

Given a set of peers with overlapping interests where each peer wishes to keep track of new documents that are relevant to their interests, we propose Shrack-a self-organizing peer-to-peer (P2P) system for document sharing and tracking. The goal of a document-tracking system is to disseminate new documents as they are published. We present a framework of Shrack and propose a gossip-like pull-only information dissemination protocol. We explore and develop mechanisms to enable a self-organizing network, based on common interest of document sets among peers. Shrack peers collaboratively share new documents of interest with other peers. Interests of peers are modeled using relevant document sets and are represented as peer profiles. There is no explicit pro file exchange between peers and no global information available. We describe how peers create their user pro files, discover the existence of other peers, locally learn about interest of other peers, and finally form a self-organizing overlay network of peers with common interests. Unlike most existing P2P file sharing systems which serve their users by finding relevant documents based on an instant query, Shrack is designed to help users that have long-term interests to keep track of relevant documents that are newly available in the system. The framework can be used as an infrastructure for any kind of documents and data, but in this thesis, we focus on research publications. We built an event-driven simulation to evaluate the performance and behaviour of Shrack. We model simulated users associated with peers after a subset of authors in the ACM digital library metadata collection. The experimental results demonstrate that the Shrack dissemination protocol is scalable as the network size increases. In addition, self-organizing overlay networks, where connections between peers are based on common interests as captured by their associated document sets, can help improve the relevance of documents received by peers in terms of F-score over random peer networks. Moreover, the resulting self-organizing networks have the characteristics of social networks.

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Tikslas. Darbo tikslas yra susipažinus su „smegenų šturmo“ metodika atlikti pasirinktos „smegenų šturmo“ PĮ analizę ir pateikti pasirinkimo rekomendacijas. Tyrimo objektas. Tyrimo objektas yra 6 diegiamos „smegenų šturmo“ programos ir 4 internetinės sistemos. Egzistuojanti problema. Renkantis „smegenų šturmo“ PĮ kyla problema, kad gamintojai nepateikia pakankamai informacijos apie savo produktą, taip pat trūksta ir informacijos lietuvių kalba. Jie pateikia technines ir programines charakteristikas, tačiau nepateikia duomenų apie funkcionalumą ir našumą, nors šie aspektai taip pat svarbūs vartotojams. / The aim of bachelor's is to review the brainstorming methodology, to carry out the analysis and give recommendation of selection to users, because manufacturers provide information about systems requirements but don’t provide information about functionality of systems. These aspects are important for users. Also there is little information in Lithuanian. While applying the research of the theoretical methods, brainstorming methodologies, rules and execution are reviewed in this paper.