Evaluating the Construction of Domain Ontologies for Recommender Systems Based on Texts

2011 ◽  
pp. 145-158
Author(s):  
Stanley Loh ◽  
Daniel Lichtnow ◽  
Thyago Borges ◽  
Gustavo Piltcher
Keyword(s):  
Text Mining ◽  
Supervised Learning ◽  
Recommender Systems ◽  
Digital Library ◽  
Recommender System ◽  
Software Tools ◽  
Domain Ontology ◽  
Engineering Process ◽  
Electronic Documents ◽  
Domain Experts

This chapter investigates different aspects in the construction of a domain ontology to a content-based recommender system. The recommender systems suggests textual electronic documents from a Digital Library, based on documents read by the users and based on textual messages posted in electronic discussions through a web chat. The domain ontology is used to represent the user’s interest and the content of the documents. In this context, the ontology is composed by a hierarchy of concepts and keywords. Each concept has a vector of keywords with weights associated. Keywords are used to identify the content of the texts (documents and messages), through the application of text mining techniques. The chapter discusses different approaches for constructing the domain ontology, including the use of text mining software tools for supervised learning, the interference of domain experts in the engineering process and the use of a normalization step.

scholarly journals DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

2021 ◽  
Author(s):  
Alberto Barbado ◽  
Víctor Fresno ◽  
Ángeles Manjarrés Riesco ◽  
Salvador Ros
Keyword(s):  
Text Mining ◽  
Recommender Systems ◽  
Semantic Features ◽  
Psychological Perspective ◽  
Spanish Poetry ◽  
Affective Meaning ◽  
Domain Experts ◽  
Specific Term ◽  
The Relationship

AbstractNowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, identifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, personality text mining studies of the authors, or the usage of poetry for therapeutic purposes.

A Methodical Text Mining on a Rationale of Domain Ontology

2019 ◽  
Author(s):  
Bhavan Kumar B ◽  
Vishal B L S R K ◽  
Bhargav K.R. ◽  
Revanth V ◽  
Chintakani Sai Gireesh
Keyword(s):  
Text Mining ◽  
Domain Ontology

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

Knowledge Transfer for Entity Resolution with Siamese Neural Networks

2021 ◽  
Vol 13 (1) ◽  
pp. 1-25
Author(s):  
Michael Loster ◽  
Ioannis Koumarelas ◽  
Felix Naumann
Keyword(s):  
Knowledge Transfer ◽  
Similarity Measure ◽  
State Of The Art ◽  
Similarity Measures ◽  
Engineering Process ◽  
Domain Experts ◽  
Multiple Datasets ◽  
Multiple Data ◽  
Domain Expertise ◽  
F Measure

The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity—duplicates—into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.

scholarly journals Generating Collaborative Systems for Digital Libraries: a Model-Driven Approach

2010 ◽  
Vol 29 (4) ◽  
pp. 171 ◽  
Author(s):  
Alessio Malizia ◽  
Paolo Bottoni ◽  
S. Levialdi
Keyword(s):  
Digital Library ◽  
User Interfaces ◽  
Digital Libraries ◽  
Automatic Generation ◽  
Visual Language ◽  
Domain Experts ◽  
Model Driven ◽  
Neutral Models ◽  
Definition Of ◽  
High Level

The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

Intelligent POIs Recommender System Based on Time Series Analysis with Seasonal Adjustment

2021 ◽  
Vol 2 (2) ◽  
pp. 66-80
Author(s):  
Meng-Kuan Chen ◽  
Hsin-Wen Wei ◽  
Wei-Tsong Lee
Keyword(s):  
Time Series ◽  
Recommender Systems ◽  
Recommender System ◽  
Research Articles ◽  
Seasonal Adjustment ◽  
Huge Amount ◽  
Travel Information ◽  
Seasonal Factor ◽  
Correct Location ◽  
Travel Recommendation

Recommender systems have been applied on a variety of applications including movies, music, news, books, research articles, search queries, and travel information. Instead of searching travel information from the extremely huge amount of travel data, a personalized travel recommender system is desired. However, an inappropriate travel recommendation may result from a wrong season, even if it is already a correct location. The current recommender systems from time to time make an inappropriate commendation without considering the seasonal factor. In order to resolve the discrepancy, the seasonal factor should have been taken into consideration when making a good travel recommender system. Therefore, this study has taken the trend analysis, time series, and seasonal factor into considerations to cope with the above mentioned discrepancy and to make the travel recommender system renders a better fit.

Sign in / Sign up

Export Citation Format

Share Document