Before making a purchase, more and more consumers in recent years are consulting other consumers¡¦ product reviews online, to assist them in making a purchasing decision. However, due to the massive amount of online reviews, consumers can hardly get useful information effectively. Hence, information overload has become a problem. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they need for specific information. Nevertheless, the returned pages from these search engines are still beyond the visual capacity of humans. Therefore, this study aims to develop a new concept of personalized recommendation based on consumer product reviews to solve the afore-mentioned problem. A series of laboratory experiment examines the effectiveness of the proposed approach and compares this approach with other traditional approaches on precision of recommendation. Meanwhile, the meaning of the recommendation behind each approach is explained. Lastly, the prototype of recommendation system based on the proposed approach is illustrated. Our system can display the trend of the gathered consumer reviews in a graphical way, such as a product satisfaction run chart. The development of recommendation systems is not only beneficial to consumers, but also advantageous to sellers.

In terms of performance measurement on Recommendation Systems, previous research focuses on system viewpoints. For Example, Leng¡¦s NewsWeeder(1995) measure recommendation performance by precision¡FSyskill&Webert measure recommendation performance by classification accuracy¡FGroupLen measure recommendation performance by system response time¡CWe bring up a user-oriented viewpoint which means that whether the recommendation interface satisfies user¡¦s needs, whether it is easy to use, and whether it provides sufficient information to user. In the meantime, prvious research didn¡¦t think over the difference of everyone¡¦s information processing style¡CTherefore, our research objective focuses on effect of cognitive styles and recommendation style on user satisfaction¡C In the construct of recommendation interface, we adopt average rating and text comment¡CAnd in the construct of cognitive style, we classify it with intuitive and analytical users¡CThe measurement of user satisfaction adopts Doll and Torkzadea (1988) questionnaire and refines it¡C The research result finds that different recommendation interfaces and cognitive styles have a significant impact on user satisfaction¡CIf we don¡¦t think over effect of cognitive styles, there is higher user satisfaction on text comment¡CIf we think over effect of cognitive styles, intuitive user has higher user satisfaction on average rating¡Fanalytical user has higher user satisfaction on text comment¡COur research contribution is as follow¡CIn academic aspect, our research finding can provide researcher in cognitive psychology¡Binformation recommendation and information management field for further research¡FIn practical aspect, our research finding can assist webstore company in implementing recommendation service¡C

Literature digital libraries is perhaps one of the most important resources to research as the preserved literature data is vital to any researchers and practitioners who need to now what people have done previously in a particular area. The emergence of World Wide Web (www) further boosts the circulation power of literature digital libraries, and people who are interested in a particular topic may easily find related articles by searching a literature digital library that provides a www interface. However, it is quite often that a given search condition will 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 literature digital libraries are equipped with a recommendation subsystem that recommend articles to a user based on his past or current interest. In this thesis, we adapt the existing approaches for web page recommendation to the recommendation of literature digital libraries. We have investigated issues for article recommendation of a literature digital library. We have developed a recommendation framework in this context that makes use of web log of a literature digital library. This framework consists of three sequential steps: data preparation of the web log, association discovery, and article recommendations. We proposed three alternatives in identifying transactions from a web log, adapted the MSApriori algorithm for discovery large itemsets, and discussed two approaches, namely hypergraph and association based recommendations, for making recommendation. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found that query-chosen and session-chosen are better methods for transaction identification, and hypergraph based approach yields better quality of article recommendation and has stable running time.

Along with the fashion of personal marketing, the traditional print media make a breakthrough by taking the advantage of digital technology to leap over the limitation of time and space. Take eDM as an example, the brand-new advertising technique that arranges bills, expenditure records and other personal information in group with the eDM, results in significant recommendation effect. Meanwhile it eradicates the prejudice that eDM is nothing more than spam and makes the eDM fresh and new! Therefore the recommendation effect appears. In the recent years, numbers of marketing researches pay much attention to the classical intimacy theory and put it into applications. Furthermore, the positive effect of personalization is verified by academical researches and business practices. Based on the manipulation of personalization and the theoretical application, this study is objective to confer the recommendation effect of personalized eDM and the mediating effect through intimacy. A two-stage survey by questionnaires was used with these samples of 490 ordinary consumers which are capable of using E-mail, and data were analyzed using the SPSS and AMOS for statistical tests. The results reveal that 1.the significant factor in influencing on intimacy is the personalization of eDM; 2.the significant factor in influencing on recommendation effect is the intimacy conceived by the eDM reader; 3.in the causal relationship between personalization and recommendation effect, intimacy is indeed an indispensable mediator. In the end, according to the findings, we draw upon some suggestions and limitations for future research.

Because electronic commerce has been flourishing in recent year, the amount and the variety of information on the web have also been rapidly increasing. However, many problems occur as the result of information overload. This thesis is to study the issue of information overload in the field of multimedia that covers not only medium of diffuse knowledge but also entertainment of everyday life. The main goal of this work is to use personalized recommendation technologies to help users select multimedia he is interested in. The thesis investigates two types of personalized recommendation: tag-based recommendation and context-aware recommendation. Regarding the former kind of recommendation, Folksonomy is the popular Web2.0 application that allows users tagging items to indicate the corresponding characteristics. These tags, provided by the users, directly or indirectly reflect his personal interests. Therefore the recommendation performance is enhanced when the tags are used with computational methods. On the other hand, the latter kind focuses on the contents and the relevant situations, because what multimedia is considered suitable for users can be different under different situations. The advantages of the personalized recommendation technology can improve performance of recommendation and take the context into account at the same time. Meanwhile this study also implements a working system for personalized multimedia recommendation.

The Recommendation Architecture (RA) model is a new connectionist approach simulating some aspects of the human brain. Application of the RA to a real world problem is a novel research problem and has not been previously addressed in literature. Research conducted with simulated data has shown much promise for the Recommendation Architecture model’s ability in pattern discovery and pattern recognition. This thesis investigates the application of the RA model for text mining where pattern discovery and recognition play an important role. The clustering system of the RA model is examined in detail and a formal notation for representing the fundamental components and algorithms is proposed for clarity of understanding. A software simulation of the clustering system of the RA model is built for empirical studies. In the argument that the RA model is applicable for text mining the following aspects of the model are examined. With its pattern recognition ability the clustering system of the RA is adapted for text classification and text organization. As the core of the RA model is concerned with pattern discovery or identification of associative similarities in input, it is also used to discover unsuspected relationships within the content of documents. How the RA model can be applied to the problems of pattern discovery in text and classification of text is addressed demonstrating results from a series of experiments. The difficulties in applying the RA model to real life data are described and several extensions to the RA model for optimal performance are proposed from the insights obtained from experiments. Furthermore, the RA model can be extended to provide user-friendly interpretation of results. This research shows that with the proposed extensions the RA model can be successfully applied to the problem of text mining to a large extent. Some limitations exist when the RA model is applied to very noisy data, which are also demonstrated here.

abstract: Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual can leverage social network to search for information that is relevant to him or her. We propose to answer this question by developing computational algorithms that analyze a user's social network. The features of the social network we analyze include the network topology and member communications of a specific user's social network. Determining the "social value" of one's contacts is a valuable outcome of this research. The algorithms we developed were tested on Twitter, which is an extremely popular social network. Twitter was chosen due to its popularity and a majority of the communications artifacts on Twitter is publically available. In this work, the social network of a user refers to the "following relationship" social network. Our algorithm is not specific to Twitter, and is applicable to other social networks, where the network topology and communications are accessible. My approaches are as follows. For a user interested in using the system, I first determine the immediate social network of the user as well as the social contacts for each person in this network. Afterwards, I establish and extend the social network for each user. For each member of the social network, their tweet data are analyzed and represented by using a word distribution. To accomplish this, I use WordNet, a popular lexical database, to determine semantic similarity between two words. My mechanism of search combines both communication distance between two users and social relationships to determine the search results. Additionally, I developed a search interface, where a user can interactively query the system. I conducted preliminary user study to evaluate the quality and utility of my method and system against several baseline methods, including the default Twitter search. The experimental results from the user study indicate that my method is able to find relevant people and identify valuable contacts in one's social circle based on the query. The proposed system outperforms baseline methods in terms of standard information retrieval metrics. / Dissertation/Thesis / M.S. Computer Science 2010

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-21T16:47:42Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dvsTeseBiblioteca.pdf: 6571192 bytes, checksum: eb7914e5ffef25b8f01ff92d9a60c164 (MD5) / Made available in DSpace on 2017-02-21T16:47:42Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) dvsTeseBiblioteca.pdf: 6571192 bytes, checksum: eb7914e5ffef25b8f01ff92d9a60c164 (MD5) Previous issue date: 2016-07-21 / FACEPE / Traditionally, single-domain recommender systems (SDRS) have achieved good results in recommending relevant items for users in order to solve the information overload problem. However, cross-domain recommender systems (CDRS) have emerged aiming to enhance SDRS by achieving some goals such as accuracy improvement, diversity, addressing new user and new item problems, among others. Instead of treating each domain independently, CDRS use knowledge acquired in a source domain (e.g. books) to improve the recommendation in a target domain (e.g. movies). Likewise SDRS research, collaborative filtering (CF) is considered the most popular and widely adopted approach in CDRS, because its implementation for any domain is relatively simple. In addition, its quality of recommendation is usually higher than that of content-based filtering (CBF) algorithms. In fact, the majority of the cross-domain collaborative filtering RS (CD-CFRS) can give better recommendations in comparison to single domain collaborative filtering recommender systems (SD-CFRS), leading to a higher users’ satisfaction and addressing cold-start, sparsity, and diversity problems. However, CD-CFRS may not necessarily be more accurate than SD-CFRS. On the other hand, context-aware recommender systems (CARS) deal with another relevant topic of research in the recommender systems area, aiming to improve the quality of recommendations too. Different contextual information (e.g., location, time, mood, etc.) can be leveraged in order to provide recommendations that are more suitable and accurate for a user depending on his/her context. In this way, we believe that the integration of techniques developed in isolation (cross-domain and contextaware) can be useful in a variety of situations, in which recommendations can be improved by information from different sources as well as they can be refined by considering specific contextual information. In this thesis, we define a novel formulation of the recommendation problem, considering both the availability of information from different domains (source and target) and the use of contextual information. Based on this formulation, we propose the integration of cross-domain and context-aware approaches for a novel recommender system (CD-CARS). To evaluate the proposed CD-CARS, we performed experimental evaluations through two real datasets with three different contextual dimensions and three distinct domains. The results of these evaluations have showed that the use of context-aware techniques can be considered as a good approach in order to improve the cross-domain recommendation quality in comparison to traditional CD-CFRS. / Tradicionalmente, “sistemas de recomendação de domínio único” (SDRS) têm alcançado bons resultados na recomendação de itens relevantes para usuários, a fim de resolver o problema da sobrecarga de informação. Entretanto, “sistemas de recomendação de domínio cruzado” (CDRS) têm surgido visando melhorar os SDRS ao atingir alguns objetivos, tais como: “melhoria de precisão”, “melhor diversidade”, abordar os problemas de “novo usuário” e “novo item”, dentre outros. Ao invés de tratar cada domínio independentemente, CDRS usam conhecimento adquirido em um domínio fonte (e.g. livros) a fim de melhorar a recomendação em um domínio alvo (e.g. filmes). Assim como acontece na área de pesquisa sobre SDRS, a filtragem colaborativa (CF) é considerada a técnica mais popular e amplamente utilizada em CDRS, pois sua implementação para qualquer domínio é relativamente simples. Além disso, sua qualidade de recomendação é geralmente maior do que a dos algoritmos baseados em filtragem de conteúdo (CBF). De fato, a maioria dos “sistemas de recomendação de domínio cruzado” baseados em filtragem colaborativa (CD-CFRS) podem oferecer melhores recomendações em comparação a “sistemas de recomendação de domínio único” baseados em filtragem colaborativa (SD-CFRS), aumentando o nível de satisfação dos usuários e abordando problemas tais como: “início frio”, “esparsidade” e “diversidade”. Entretanto, os CD-CFRS podem não ser mais precisos do que os SD-CFRS. Por outro lado, “sistemas de recomendação sensíveis à contexto” (CARS) tratam de outro tópico relevante na área de pesquisa de sistemas de recomendação, também visando melhorar a qualidade das recomendações. Diferentes informações contextuais (e.g. localização, tempo, humor, etc.) podem ser utilizados a fim de prover recomendações que são mais adequadas e precisas para um usuário dependendo de seu contexto. Desta forma, nós acreditamos que a integração de técnicas desenvolvidas separadamente (de “domínio cruzado” e “sensíveis a contexto”) podem ser úteis em uma variedade de situações, nas quais as recomendações podem ser melhoradas a partir de informações obtidas em diferentes fontes além de refinadas considerando informações contextuais específicas. Nesta tese, nós definimos uma nova formulação do problema de recomendação, considerando tanto a disponibilidade de informações de diferentes domínios (fonte e alvo) quanto o uso de informações contextuais. Baseado nessa formulação, nós propomos a integração de abordagens de “domínio cruzado” e “sensíveis a contexto” para um novo sistema de recomendação (CD-CARS). Para avaliar o CD-CARS proposto, nós realizamos avaliações experimentais através de dois “conjuntos de dados” com três diferentes dimensões contextuais e três domínios distintos. Os resultados dessas avaliações mostraram que o uso de técnicas sensíveis a contexto pode ser considerado como uma boa abordagem a fim de melhorar a qualidade de recomendações de “domínio cruzado” em comparação às recomendações de CD-CFRS tradicionais.

Personalized recommender systems aim to assist users in retrieving and accessing interesting items by automatically acquiring user preferences from the historical data and matching items with the preferences. In the last decade, recommendation services have gained great attention due to the problem of information overload. However, despite recent advances of personalization techniques, several critical issues in modern recommender systems have not been well studied. These issues include: (1) understanding the accessing patterns of users (i.e., how to effectively model users' accessing behaviors); (2) understanding the relations between users and other objects (i.e., how to comprehensively assess the complex correlations between users and entities in recommender systems); and (3) understanding the interest change of users (i.e., how to adaptively capture users' preference drift over time). To meet the needs of users in modern recommender systems, it is imperative to provide solutions to address the aforementioned issues and apply the solutions to real-world applications. The major goal of this dissertation is to provide integrated recommendation approaches to tackle the challenges of the current generation of recommender systems. In particular, three user-oriented aspects of recommendation techniques were studied, including understanding accessing patterns, understanding complex relations and understanding temporal dynamics. To this end, we made three research contributions. First, we presented various personalized user profiling algorithms to capture click behaviors of users from both coarse- and fine-grained granularities; second, we proposed graph-based recommendation models to describe the complex correlations in a recommender system; third, we studied temporal recommendation approaches in order to capture the preference changes of users, by considering both long-term and short-term user profiles. In addition, a versatile recommendation framework was proposed, in which the proposed recommendation techniques were seamlessly integrated. Different evaluation criteria were implemented in this framework for evaluating recommendation techniques in real-world recommendation applications. In summary, the frequent changes of user interests and item repository lead to a series of user-centric challenges that are not well addressed in the current generation of recommender systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective recommendation framework.

This thesis deals with the analysis of the ALUMA ČS, s.r.o. company information system, which then serves as the basis for several designs, created to improve the current situation. These designs are the key part of this work. The theoretical part of this work focuses on explaining the given issues, mainly ERP systems and related areas. The practical part focuses on the aforementioned analysis, which deals with processes, IT equipment and systems used in the company, as well as on the designs for the information system.