Combining collaborative and content-based filtering to recommend research papers

The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available items, according to users’ interests. This research focuses on the two most prevalent techniques to date, namely Content-Based Filtering and Collaborative Filtering. The first explores the text of the paper itself, recommending items similar in content to the ones the user has rated in the past. The second explores the citation web existing among papers. As these two techniques have complementary advantages, we explored hybrid approaches to recommending research papers. We created standalone and hybrid versions of algorithms and evaluated them through both offline experiments on a database of 102,295 papers, and an online experiment with 110 users. Our results show that the two techniques can be successfully combined to recommend papers. The coverage is also increased at the level of 100% in the hybrid algorithms. In addition, we found that different algorithms are more suitable for recommending different kinds of papers. Finally, we verified that users’ research experience influences the way users perceive recommendations. In parallel, we found that there are no significant differences in recommending papers for users from different countries. However, our results showed that users’ interacting with a research paper Recommender Systems are much happier when the interface is presented in the user’s native language, regardless the language that the papers are written. Therefore, an interface should be tailored to the user’s mother language.

An Ontology- Content-based Filtering Method

Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users' and the items' profiles with concepts taken from the same vocabulary. We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related" concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users' profiles and the items' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies.

Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Explicit context matching in content-based publish/subscribe systems

Although context could be exploited to improve performance, elasticity and adaptation in most distributed systems that adopt the publish/subscribe (P/S) communication model, only a few researchers have focused on the area of context-aware matching in P/S systems and have explored its implications in domains with highly dynamic context like wireless sensor networks (WSNs) and IoT-enabled applications. Most adopted P/S models are context agnostic or do not differentiate context from the other application data. In this article, we present a novel context-aware P/S model. SilboPS manages context explicitly, focusing on the minimization of network overhead in domains with recurrent context changes related, for example, to mobile ad hoc networks (MANETs). Our approach represents a solution that helps to efficiently share and use sensor data coming from ubiquitous WSNs across a plethora of applications intent on using these data to build context awareness. Specifically, we empirically demonstrate that decoupling a subscription from the changing context in which it is produced and leveraging contextual scoping in the filtering process notably reduces (un)subscription cost per node, while improving the global performance/throughput of the network of brokers without fltering the cost of SIENA-like topology changes.

Social Recommendation Based on Content and Trust

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

How to integrate stories and ICT in content-based units of work for English learning

The aim of this article is to show how it is possible to integrate stories and ICT in Content Language Integrated Learning (CLIL) for English as a foreign language (EFL) learning in bilingual schools. Two Units of Work are presented. One, for the second year of Primary, is based on a Science topic, ‘Materials’. The story used is ‘The three little pigs’ and the computer program ‘JClic’. The other one is based on a Science and Arts topic for the sixth year of Primary, the story used is ‘Charlotte’s Web’ and the computer program ‘Atenex’.

Tuning metadata for better movie content-based recommendation systems

The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

Yield of essential oil and safrole content based on fresh and dry biomass of long pepper in the Brazilian Amazon

Long pepper (Piper hispidinervum) is an Amazonian species of commercial interest due to the production of safrole. Drying long pepper biomass to extract safrole is a time consuming and costly process that can also result in the contamination of the material by microorganisms. The objective of this study was to analyze the yield of essential oil and safrole content of fresh and dried biomass of long pepper accessions maintained in the Active Germoplasm Bank of Embrapa Acre, in the state of Acre, Brazil, aiming at selecting genotypes with best performance on fresh biomass to recommend to the breeding program of the species. Yield of essential oil and safrole content were assessed in 15 long pepper accessions. The essential oil extraction was performed by hydrodistillation and analyzed by gas chromatography. A joint analysis of experiments was performed and the means of essential oil yield and safrole content for each biomass were compared by Student's t-test. There was variability in the essential oil yield and safrole content. There was no difference between the types of biomass for oil yield; however to the safrole content there was difference. Populations 9, 10, 12 and 15 had values of oil yield between 4.1 and 5.3%, and safrole content between 87.2 and 94.3%. The drying process does not interfere in oil productivity. These populations have potential for selection to the long pepper breeding program using oil extraction in the fresh biomass

CONSUMER PERCEPTIONS ON A CONTENT-BASED DIGITAL SERVICE. The usage and service quality of Masennusinfo.fi

Kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita jatkuvasti saadakseen lisää tietoa terveydestään. Samalla he arvioivat käyttämiensä palveluiden laatua. Jotta yritykset voisivat suunnitella ja tarjota parhaita mahdollisia digitaalisia palveluita kuluttajille, yritysten tulisi tunnistaa ja analysoida kuluttajien kokemuksia ja käyttötarkoituksia heidän palveluissaan. Tämän tutkimuksen tarkoituksena on kuvailla kuluttajien näkemyksiä Masennusinfo.fi:stä, joka on sisältöpohjainen digitaalinen palvelu, ja joka tarjoaa käyttäjilleen tietoa masennuksesta. Päämääränä on selvittää, kuinka kuluttajat kokevat lääkeyrityksen tarjoaman palvelun laadun ja mihin tarkoituksiin sitä käytetään. Tutkimuksen tarkoitus voidaan jakaa kolmeen osa-ongelmaan: • Mihin tarkoituksiin kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita? • Miten kuluttajat kokevat näiden palveluiden laadun? • Kuinka käyttötarkoitus ja koettu laatu eroavat eri käyttäjäryhmissä? Tutkimus toteutetaan web-pohjaisella kyselytutkimuksella. Mittarit tehdään teoreettisen viitekehyksen pohjalta, joka perustuu aikaisempaan tutkimukseen. Tutkimuksen empiirinen osuus suoritetaan pop-up tutkimuksella, joka sijoitetaan tutkittavalle sivustolle antaen näin kaikille palvelun käyttäjille mahdollisuuden vastata kyselyyn. Tulokset osoittavat, että palvelua käyttävät suurimmaksi osaksi naiset, suhteellisen nuoret 16−29-vuotiaat, tai yli keski-ikäiset 50−65-vuotiaat henkilöt, jotka ovat joko työssäkäyviä tai opiskelijoita ja korkeasti koulutettuja. Masennusinfo.fi nähdään laadukkaana palveluna kaikissa käyttäjäryhmissä sekä sen käytettävyyden että sisällön perusteella. Käyttötarkoituksetkin ovat jokseenkin samankaltaisia eri käyttäjäryhmissä. Yleensä palvelua käytetään tiedon hakemiseen sairauden alkuvaiheessa. Löydöksien perusteella esitetään, että palvelua muokataan vastaamaan yhä paremmin sen käyttötarkoituksia ja tyypillistä käyttäjäprofiilia. Koska muutamia pieniä eroja käyttäjäryhmien näkemyksissä havaittiin, palveluiden tuottaja päättää, minkä ryhmän mieltymyksiä se noudattaa.

Consumer perceptions on a content-based digital service:the usage and service quality of Masennusinfo.fi

Kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita jatkuvasti saadakseen lisää tietoa terveydestään. Samalla he arvioivat käyttämiensä palveluiden laatua. Jotta yritykset voisivat suunnitella ja tarjota parhaita mahdollisia digitaalisia palveluita kuluttajille, yritysten tulisi tunnistaa ja analysoida kuluttajien kokemuksia ja käyttötarkoituksia heidän palveluissaan. Tämän tutkimuksen tarkoituksena on kuvailla kuluttajien näkemyksiä Masennusinfo.fi:stä, joka on sisältöpohjainen digitaalinen palvelu, ja joka tarjoaa käyttäjilleen tietoa masennuksesta. Päämääränä on selvittää, kuinka kuluttajat kokevat lääkeyrityksen tarjoaman palvelun laadun ja mihin tarkoituksiin sitä käytetään. Tutkimuksen tarkoitus voidaan jakaa kolmeen osa-ongelmaan: Mihin tarkoituksiin kuluttajat käyttävät sisältöpohjaisia digitaalisia palveluita? Miten kuluttajat kokevat näiden palveluiden laadun? Kuinka käyttötarkoitus ja koettu laatu eroavat eri käyttäjäryhmissä? Tutkimus toteutetaan web-pohjaisella kyselytutkimuksella. Mittarit tehdään teoreettisen viitekehyksen pohjalta, joka perustuu aikaisempaan tutkimukseen. Tutkimuksen empiirinen osuus suoritetaan pop-up tutkimuksella, joka sijoitetaan tutkittavalle sivustolle antaen näin kaikille palvelun käyttäjille mahdollisuuden vastata kyselyyn. Tulokset osoittavat, että palvelua käyttävät suurimmaksi osaksi naiset, suhteellisen nuoret 16−29- vuotiaat, tai yli keski-ikäiset 50−65-vuotiaat henkilöt, jotka ovat joko työssäkäyviä tai opiskelijoita ja korkeasti koulutettuja. Masennusinfo.fi nähdään laadukkaana palveluna kaikissa käyttäjäryhmissä sekä sen käytettävyyden että sisällön perusteella. Käyttötarkoituksetkin ovat jokseenkin samankaltaisia eri käyttäjäryhmissä. Yleensä palvelua käytetään tiedon hakemiseen sairauden alkuvaiheessa. Löydöksien perusteella esitetään, että palvelua muokataan vastaamaan yhä paremmin sen käyttötarkoituksia ja tyypillistä käyttäjäprofiilia. Koska muutamia pieniä eroja käyttäjäryhmien näkemyksissä havaittiin, palveluiden tuottaja päättää, minkä ryhmän mieltymyksiä se noudattaa.

Development of Shape Descriptors Based on Legendre Polynomials and Content-Based Retrieval of Scoliosis Images

The wealth of information available freely on the web and medical image databases poses a major problem for the end users: how to find the information needed? Content –Based Image Retrieval is the obvious solution.A standard called MPEG-7 was evolved to address the interoperability issues of content-based search.The work presented in this thesis mainly concentrates on developing new shape descriptors and a framework for content – based retrieval of scoliosis images.New region-based and contour based shape descriptor is developed based on orthogonal Legendre polymomials.A novel system for indexing and retrieval of digital spine radiographs with scoliosis is presented.

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.