A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.

Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper.

AS INOVAÇÕES TECNOLÓGICAS E OS INTERMEDIÁRIOS DA PRODUÇÃO E DO ACESSO À INFORMAÇÃO JORNALÍSTICA

O jornalismo é um dos principais meios de oferta de temas para a discussão e formação da opinião pública, porém depende de um sistema técnico para ser transmitido. Durante mais de cem anos as informações produzidas pela imprensa foram emitidas, armazenadas, transmitidas e recebidas pelos chamados veículos de comunicação de massa que utilizam a rede centralizada cujas características estão na escassez material, produção em série e massificação. Esse sistema separa no tempo e no espaço emissores e receptores criando uma relação desigual de força em que as grandes empresas controlaram o fluxo informativo, definindo quais fatos seriam veiculados como notícia. Em 1995, a internet cuja informação circula sob a tecnologia da rede distribuída, foi apropriada pela sociedade, alterando a forma de produção, armazenamento e transmissão de informação. A tecnologia despertou a esperança de que esta ferramenta poderia proporcionar uma comunicação mais dialógica e democrática. Mas aos poucos pode-se perceber novas empresas se apropriando da tecnologia da rede distribuída sob a qual circula a internet, gerando um novo controle do fluxo informativo. Realizou-se nessa pesquisa um levantamento bibliográfico para estabelecer uma reflexão crítica dos diferentes intermediários entre fato e a notícia tanto da rede centralizada como na rede distribuída, objetivando despertar uma discussão que possa oferecer novas ideias para políticas, bem como alternativas para uma comunicação mais democrática e mais libertária.

Collaborative personal profiling for web service ranking and recommendation

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

Assimilation and contrast in web product reviews: devaluing the recommendation of a proficient but dissimilar reviewer

When viewing web-consumer reviews consumers encounter the reviewers in an anonymous environment. Although their interactions are only virtual they still exchange social information, e.g. often reviewers refer to their proficiency or consumption motives within the review texts. Do these social information harm the viewers’ perception of the recommended products? The present study addresses this question by applying the paradigm of social comparison (Mussweiler, 2003) to web-consumer reviews. In a laboratory experiment with a student sample (n = 120) we manipulated the perceived similarity between reviewer and viewer and the perceived proficiency of the reviewer. A measurement of achievement goals (Elliott & McGregor, 2001) and average number of hours of study prior to the experiment allowed to introduce the reviewer as high [low] in proficiency and similar [dissimilar] in achievement goals. As predicted, the viewer’s evaluation of the recommended products differed as a function of this social information. Contrasting with the reviewer led to devaluing the products recommended by a proficient but dissimilar reviewer. However, against our prediction social comparison with the reviewer did not affect the viewer`s self-evaluation. Whether social information in web-product reviews affects the viewer`s self-evaluation and induces both social comparison processes remains an open question. Future studies aim to address this by manipulating the informational focus of the viewer, rather than the perceived similarity between viewer and reviewer. So far, the present study extends the application of social comparison to consumption environments and contributes to the understanding of the virtual social identity.

Shopping center tracking and recommendation systems

Shopping centers present a rich and heterogeneous environment, where IT systems can be implemented in order to support the needs of its actors. However, due to the environment complexity, several feasibility issues emerge when designing both the logical and physical architecture of such systems. Additionally, the system must be able to cope with the individual needs of each actor, and provide services that are easily adopted by them, taking into account several sociological and economical aspects. In this sense, we present an overview of current support systems for shopping center environments. From this overview, a high-level model of the domain (involving actors and services) is described along with challenges and possible features in the context of current Semantic Web, mobile device and sensor technologies.

GuideMe: service for consulting, publication and recommendation of touristic locations

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Traceability-based access recommendation

Devido à grande quantidade de dados disponíveis na Internet, um dos maiores desafios no mundo virtual é recomendar informação aos seus utilizadores. Por outro lado, esta grande quantidade de dados pode ser útil para melhorar recomendações se for anotada e interligada por dados de proveniência. Neste trabalho é abordada a temática de recomendação de (alteração de) permissões acesso sobre recursos ao seu proprietário, ao invés da recomendação do próprio recurso a um potencial consumidor/leitor. Para permitir a recomendação de acessos a um determinado recurso, independentemente do domínio onde o mesmo se encontra alojado, é essencial a utilização de sistemas de controlo de acessos distribuídos, mecanismos de rastreamento de recursos e recomendação independentes do domínio. Assim sendo, o principal objectivo desta tese é utilizar informação de rastreamento de acções realizadas sobre recursos (i.e. informação que relaciona recursos e utilizadores através da Web independentemente do domínio de rede) e utiliza-la para permitir a recomendação de privilégios de acesso a esses recursos por outros utilizadores. Ao longo do desenvolvimento da tese resultaram as seguintes contribuições: A análise do estado da arte de recomendação e de sistemas de recomendação potencialmente utilizáveis na recomendação de privilégios (secção 2.3); A análise do estado da arte de mecanismos de rastreamento e proveniência de informação (secção 2.2); A proposta de um sistema de recomendação de privilégios de acesso independente do domínio e a sua integração no sistema de controlo de acessos proposto anteriormente (secção 3.1); Levantamento, análise e especificação da informação relativa a privilégios de acesso, para ser utilizada no sistema de recomendação (secção 2.1); A especificação da informação resultante do rastreamento de acções para ser utilizada na recomendação de privilégios de acesso (secção 4.1.1); A especificação da informação de feedback resultante do sistema de recomendação de acessos e sua reutilização no sistema de recomendação(secção 4.1.3); A especificação, implementação e integração do sistema de recomendação de privilégios de acesso na plataforma já existente (secção 4.2 e secção 4.3); Realização de experiências de avaliação ao sistema de recomendação de privilégios, bem como a análise dos resultados obtidos (secção 5).

TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services

he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.

A RecommendationSystem for a Web Shop

Tämän työn tarkoituksena on käytännöllisen suositusjärjestelmäratkaisun kehittäminen verkkokauppaympäristöön olemassaolevaa teoriatietoa käyttäen. Työn ensimmäisessä osiossa tarkastellaan ensin tapoja lähdetiedon keräämiseksi järjestelmää varten. Tämän jälkeen käydään läpi eri menetelmiä suosituksen toteuttamiseksi. Lisäksi tutustutaan yleisiin ongelmiin eri menetelmien kanssa. Seuraavaksi tutkitaan miten järjestelmän käyttämään suositustietoa voidaan ryhmitellä. Tämänjälkeen arvioidaan esitettyjä menetelmiä yleisesti tunnettujen kriteerien perusteella. Suositusjärjestelmän toteutustyö on kuvattuna työn toisessa osiossa. Toteutettu ohjelmisto on asennettu kahteen erilliseen toimintaympäristöön.

Managing Quality Properties of Web Service Compositions

Web services from different partners can be combined to applications that realize a more complex business goal. Such applications built as Web service compositions define how interactions between Web services take place in order to implement the business logic. Web service compositions not only have to provide the desired functionality but also have to comply with certain Quality of Service (QoS) levels. Maximizing the users' satisfaction, also reflected as Quality of Experience (QoE), is a primary goal to be achieved in a Service-Oriented Architecture (SOA). Unfortunately, in a dynamic environment like SOA unforeseen situations might appear like services not being available or not responding in the desired time frame. In such situations, appropriate actions need to be triggered in order to avoid the violation of QoS and QoE constraints. In this thesis, proper solutions are developed to manage Web services and Web service compositions with regard to QoS and QoE requirements. The Business Process Rules Language (BPRules) was developed to manage Web service compositions when undesired QoS or QoE values are detected. BPRules provides a rich set of management actions that may be triggered for controlling the service composition and for improving its quality behavior. Regarding the quality properties, BPRules allows to distinguish between the QoS values as they are promised by the service providers, QoE values that were assigned by end-users, the monitored QoS as measured by our BPR framework, and the predicted QoS and QoE values. BPRules facilitates the specification of certain user groups characterized by different context properties and allows triggering a personalized, context-aware service selection tailored for the specified user groups. In a service market where a multitude of services with the same functionality and different quality values are available, the right services need to be selected for realizing the service composition. We developed new and efficient heuristic algorithms that are applied to choose high quality services for the composition. BPRules offers the possibility to integrate multiple service selection algorithms. The selection algorithms are applicable also for non-linear objective functions and constraints. The BPR framework includes new approaches for context-aware service selection and quality property predictions. We consider the location information of users and services as context dimension for the prediction of response time and throughput. The BPR framework combines all new features and contributions to a comprehensive management solution. Furthermore, it facilitates flexible monitoring of QoS properties without having to modify the description of the service composition. We show how the different modules of the BPR framework work together in order to execute the management rules. We evaluate how our selection algorithms outperform a genetic algorithm from related research. The evaluation reveals how context data can be used for a personalized prediction of response time and throughput.

The Audiomomma Music Recommendation System

We design and implement a system that recommends musicians to listeners. The basic idea is to keep track of what artists a user listens to, to find other users with similar tastes, and to recommend other artists that these similar listeners enjoy. The system utilizes a client-server architecture, a web-based interface, and an SQL database to store and process information. We describe Audiomomma-0.3, a proof-of-concept implementation of the above ideas.