The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0.

Integrating collaborative filtering and matching-based search for product recommendation

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment <user, item, tag>, should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Overview of INEX 2014

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2014 evaluation campaign, which consisted of three tracks: The Interactive Social Book Search Track investigated user information seeking behavior when interacting with various sources of information, for realistic task scenarios, and how the user interface impacts search and the search experience. The Social Book Search Track investigated the relative value of authoritative metadata and user-generated content for search and recommendation using a test collection with data from Amazon and LibraryThing, including user profiles and personal catalogues. The Tweet Contextualization Track investigated tweet contextualization, helping a user to understand a tweet by providing him with a short background summary generated from relevant Wikipedia passages aggregated into a coherent summary. INEX 2014 was an exciting year for INEX in which we for the third time ran our workshop as part of the CLEF labs. This paper gives an overview of all the INEX 2014 tracks, their aims and task, the built test-collections, the participants, and gives an initial analysis of the results.

An efficient tagging data interpretation and representation scheme for item recommendation

A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem.

Serendipitous road trips: Enhancing tourists’ experiences through social interaction

Driving can be a lonely activity. While there has been a lot of research and technical inventions concerning car-to-car communication and passenger entertainment, there is still little work concerning connecting drivers. Whereas tourism is very much a social activity, drive tourists have few options to communicate with fellow travellers. The proposed project is placed at the intersection of tourism and driving and aims to enhance the trip experience during driving through social interaction. This thesis explores how a mobile application that allows instant messaging between travellers sharing similar context can add to road trip experiences. To inform the design of such an application, the project adopted the principle of the user-centred design process. User needs were assessed by running an ideation workshop and a field trip. Findings of both studies have shown that tourists have different preferences and diverse attitudes towards contacting new people. Yet all participants stressed the value of social recommendations. Based on those results and a later expert review, three prototype versions of the system were created. A prototyping session with potential end users highlighted the most important features including the possibility to view user profiles, choose between text and audio input and receive up-to-date information. An implemented version of the prototype was evaluated in an exploratory study to identify usability related problems in an actual use case scenario as well as to find implementation bugs. The outcomes of this research are relevant for the design of future mobile tourist guides that leverage from benefits of social recommendations.

Clustering Users of Online Content Service

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.

Understanding urban rail travel for improved patronage forecasting

Improved forecasting of urban rail patronage is essential for effective policy development and efficient planning for new rail infrastructure. Past modelling and forecasting of urban rail patronage has been based on legacy modelling approaches and often conducted at the general level of public transport demand, rather than being specific to urban rail. This project canvassed current Australian practice and international best practice to develop and estimate time series and cross-sectional models of rail patronage for Australian mainland state capital cities. This involved the implementation of a large online survey of rail riders and non-riders for each of the state capital cities, thereby resulting in a comprehensive database of respondent socio-economic profiles, travel experience, attitudes to rail and other modes of travel, together with stated preference responses to a wide range of urban travel scenarios. Estimation of the models provided a demonstration of their ability to provide information on the major influences on the urban rail travel decision. Rail fares, congestion and rail service supply all have a strong influence on rail patronage, while a number of less significant factors such as fuel price and access to a motor vehicle are also influential. Of note, too, is the relative homogeneity of rail user profiles across the state capitals. Rail users tended to have higher incomes and education levels. They are also younger and more likely to be in full-time employment than non-rail users. The project analysis reported here represents only a small proportion of what could be accomplished utilising the survey database. More comprehensive investigation was beyond the scope of the project and has been left for future work.

Disseminação de dados estatísticos georeferenciados através de WEB services e software livre.

Diferentes organizações públicas e privadas coletam e disponibilizam uma massa de dados sobre a realidade sócio-econômica das diferentes nações. Há hoje, da parte do governo brasileiro, um interesse manifesto de divulgar uma gama diferenciada de informações para os mais diversos perfis de usuários. Persiste, contudo, uma série de limitações para uma divulgação mais massiva e democrática, entre elas, a heterogeneidade das fontes de dados, sua dispersão e formato de apresentação pouco amigável. Devido à complexidade inerente à informação geográfica envolvida, que produz incompatibilidade em vários níveis, o intercâmbio de dados em sistemas de informação geográfica não é problema trivial. Para aplicações desenvolvidas para a Web, uma solução são os Web Services que permitem que novas aplicações possam interagir com aquelas que já existem e que sistemas desenvolvidos em plataformas diferentes sejam compatíveis. Neste sentido, o objetivo do trabalho é mostrar as possibilidades de construção de portais usando software livre, a tecnologia dos Web Services e os padrões do Open Geospatial Consortium (OGC) para a disseminação de dados espaciais. Visando avaliar e testar as tecnologias selecionadas e comprovar sua efetividade foi desenvolvido um exemplo de portal de dados sócio-econômicos, compreendendo informações de um servidor local e de servidores remotos. As contribuições do trabalho são a disponibilização de mapas dinâmicos, a geração de mapas através da composição de mapas disponibilizados em servidores remotos e local e o uso do padrão OGC WMC. Analisando o protótipo de portal construído, verifica-se, contudo, que a localização e requisição de Web Services não são tarefas fáceis para um usuário típico da Internet. Nesta direção, os trabalhos futuros no domínio dos portais de informação geográfica poderiam adotar a tecnologia Representational State Transfer (REST).

Infra-estrutura para portal internet integrador de serviços

Esta tese surge no contexto de sistemas e serviços web. O objectivo é propor uma solução para o problema da integração de informação de diversas fontes, numa plataforma web única, personalizável e adaptável ao utilizador. Nos casos de empresas ou organizações que tenham para diferentes tarefas, diferentes sistemas de informação independentes, o problema da integração de informação surge com a necessidade de integração destes numa única interface disponibilizada aos seus utilizadores. A integração de serviços numa mesma interface pressupõe que haja comunicação entre um sistema central (que fornece a interface) e os diversos sistemas existentes (que detêm a totalidade – ou parte – da informação a apresentar). Assim, será necessário garantir a identidade do utilizador a cada um dos serviços apresentados, bem como assegurar que cada utilizador tem à sua disposição de forma centralizada, apenas e só a informação e operações a que realmente tem acesso em cada um dos sistemas. Trata-se de uma plataforma que pretende por um lado, fornecer a informação correcta e orientada ao utilizador e, por outro lado, garantir que a organização que suporta o sistema consegue informar e interagir com os seus utilizadores de forma mais eficaz. O cenário adoptado é a Universidade de Aveiro. Esta pretende disponibilizar uma plataforma electrónica, onde os diferentes interlocutores (alunos, docentes, funcionários, ex-alunos, etc.) possam ter acesso a informação dirigida e orientada aos seus interesses e funções na Universidade. De modo a que cada utilizador seja realmente visto como um utilizador único, serão estudados e comparados serviços de modelação de utilizador e perfis de utilizador. Será proposto um serviço de modelação de utilizador e uma lógica de criação de perfis de utilizador, distintos do existente no estado de arte. Esta lógica conjuga a personalização da interface por parte do utilizador, com a gestão de operações e definição de políticas de segurança por parte da organização, de forma independente relativamente ao sistema de informação subjacente.

How remote labs impact on course outcomes: various practices using VISIR

As technology is increasingly being seen as a facilitator to learning, open remote laboratories are increasingly available and in widespread use around the world. They provide some advantages over traditional hands-on labs or simulations. This paper presents the results of integrating the open remote laboratory VISIR into several courses, in various contexts and using various methodologies. These integrations, all related to higher education engineering, were designed by teachers with different perspectives to achieve a range of learning outcomes. The degree to which these VISIR-related outcomes were accomplished is discussed. The results reflect the levels of student engagement and learning and of teacher involvement. From the analysis, a connection between these two aspects was traced, although only related to the user profiles. VISIR is shown to be always of benefit for more motivated students, but this benefit can be maximized under particular conditions and characteristics.

Les personnes âgées face au défi d'utilisation des nouvelles technologies : étude de l’utilisabilité des interfaces de téléphones portables

L’évolution technologique et l'accroissement de la population vieillissante sont deux tendances majeures de la dernière décennie. Durant cette période, la prolifération ubiquitaire de la téléphonie mobile a changé les habitudes de communication des gens. Le changement constant des appareils téléphoniques portatifs, l'augmentation des fonctions, la diversité iconographique, la variété des interfaces et la complexité de navigation exigent aujourd’hui non seulement plus de temps d'adaptation et d’apprentissage, mais représentent aussi un effort cognitif important. Les technologies d'information et de communication (TIC) sont devenues des outils incontournables de la vie moderne. Pour les personnes âgées, cet univers en perpétuelle mutation avec ces nouveaux appareils représente un obstacle à l’accès à l’information et contribue ainsi au gap générationnel. Le manque de référence et de soutien et les déficiences physiques ou cognitives, que certaines personnes développent en vieillissant, rendent l'usage de ce type d’objet souvent impossible. Pourtant, les produits intelligents plus accessibles, tant au niveau physique que cognitif sont une réelle nécessité au sein de notre société moderne permettant aux personnes âgées de vivre de manière plus autonome et « connectée ». Cette recherche a pour but d'exposer les défis d'usage des téléphones portables existants et d'identifier en particulier les problèmes d’usage que les personnes âgées manifestent. L’étude vise la tranche de population qui est peu habituée aux technologies de communications qui ne ciblent le plus souvent que les plus jeunes et les professionnels. C’est en regardant les habitudes d’usage, que la recherche qualitative nous permettra d’établir un profil des personnes âgées par rapport au TIC et de mieux comprendre les défis liés à la perception, compréhension et l’usage des interfaces de téléphones portables.

PROTECT_U: Un système communautaire pour la protection des usagers de Facebook

Article publié dans le journal « Journal of Information Security Research ». March 2012.