New Graph Based Trust Similarity Measure

Trust network in social networks can be considered as graph which trustors and trustees are graph vertices and edges present trust between them with measured values. To evaluate trust between trustors and trustees there is some similarity measures to measure similarity between trustors together or trustees together and then by using evaluated values predict trust value between them. Similarity measure has important effect on final accuracy. In this paper we propose graph based similarity measure. Similarity between two users is computed by connection between them on graph then this computed similarity used with k- nearest neighbors method to evaluate(predict) trust between users. To the best of our knowledge this is the first work introduces graph based similarity measure, empirical results on two real datasets show accuracy of predicted trust using proposed similarity measure outperforms accuracy of method without it

A Survey of e-Commerce Recommender Systems

Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems

Recommender System Based on Expert and Item Category

The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique   2) the experts from the frequency of rating technique and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creatingThe objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating

A New Approach for Movie Recommender System using K-means Clustering and PCA

Recommendation systems are refining mechanism to envisagethe ratings for itemsand users, to recommend likes mainly from the big data. Our proposed recommendationsystem gives a mechanism to users to classify with the same interest. This recommendersystem becomes core to recommend the e-commerce and various websites applications basedon similar likes. This central idea of our work is to develop movie recommender system withthe help of clustering using K-means clustering technique and data pre-processing usingPrincipal Component Analysis (PCA). In this proposed work, new recommendationtechnique has been presented using K-means clustering, PCA and sampling with the help ofMovieLens dataset. Our proposed method and its subsequent results have been discussed andcollation with other existing methods using evaluation metrics like Dunn Index, averagesimilarity and computational time has been also explained and prove that our technique isbest among other techniques. The results achieve from the MovieLens dataset is able to provehigh efficiency and accuracy of our proposed work. Our proposed method is able to achievethe MAE of .67, which is better than other methods

A New Approach for Movie Recommender System using K-means Clustering and PCA

159-165Recommendation systems are refining mechanism to envisage the ratings for items and users, to recommend likes mainly from the big data. Our proposed recommendation system gives a mechanism to users to classify with the same interest. This recommender system becomes core to recommend the e-commerce and various websites applications based on similar likes. This central idea of our work is to develop movie recommender system with the help of clustering using K-means clustering technique and data pre-processing using Principal Component Analysis (PCA). In this proposed work, new recommendation technique has been presented using K-means clustering, PCA and sampling with the help of MovieLens dataset. Our proposed method and its subsequent results have been discussed and collation with other existing methods using evaluation metrics like Dunn Index, average similarity and computational time has been also explained and prove that our technique is best among other techniques. The results achieve from the MovieLens dataset is able to prove high efficiency and accuracy of our proposed work. Our proposed method is able to achieve the MAE of 0.67, which is better than other methods

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm

Search Results: Predicting Ranking Algorithms With User Ratings and User-Driven Data

The purpose of this correlational quantitative study was to examine the possible relationship between user-driven parameters, user ratings, and ranking algorithms. The study’s population consisted of students and faculty in the information technology (IT) field at a university in Huntington, WV. Arrow’s impossibility theorem was used as the theoretical framework for this study. Complete survey data were collected from 47 students and faculty members in the IT field, and a multiple regression analysis was used to measure the correlations between the variables. The model was able to explain 85% of the total variability in the ranking algorithm. The overall model was able to significantly predict the algorithm ranking discounted cumulative gain, R2 = .852, F(3,115) = 220.13, p \u3c .01. The Respondent DCG and Search term variables were the most significant predictor with p = .0001. The overall findings can potentially be useful to content providers who focus their content on a specific niche. The content created by these providers would most likely be focused entirely on that subgroup of interested users. While it is necessary to focus content to the interested users, it may be beneficial to expand the content to more generic terms to help reach potential new users outside of the subgroups of interest. User’s searching for more generic terms could potentially be exposed to more content that would generally require more specific search terms. This exposure with more generic terms could help users expand their knowledge of new content more quickly

Invenire: an evolutionary approach for combining results of recommender systems techniques based on collaborative filtering

Submitted by Marlene Santos ([email protected]) on 2014-12-18T18:42:03Z No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Luciana Ferreira ([email protected]) on 2014-12-22T11:15:25Z (GMT) No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2014-12-22T11:15:25Z (GMT). No. of bitstreams: 2 Dissertacao - Edjalma Queiroz da Silva - 2014.pdf: 3244366 bytes, checksum: 7c2506e59c6f1ebdc4608a6ff2ac207d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-20Recommendation systems function as a guide, helping users to discover products of interest. There are various techniques and approaches in the literature that enable the generationofrecommendations.Thisisinterestingbecauseitemphasizesthediversityof options;ontheotherhand,itcancausedoubtthesystemdesigneraboutwhichisthebest techniquetouse.Eachoftheseapproacheshasparticularitiesanddependsonthecontext to be applied. Therefore, the decision to choose between the techniques is complex to be done manually. This work proposes an evolutionary approach for combining results of recommendation techniques (Invenire) in order to automate the choice of techniques and get fewer errors in recommendations. To evaluate the proposal, experiments were performed with a dataset from MovieLens and some Collaborative Filtering techniques. The results show that the combining methodology proposed in this paper performs better than any one collaborative filtering technique separately in the context addressed. The improvement varies from 3,6% to 118,99% depending on the technique and the experiment executed.Sistemas de Recomendação funcionam como um conselheiro, comportando-se de tal formaaorientaraspessoasnadescobertadeprodutosdeinteresse.Existemváriastécnicas eabordagensnaliteraturaquepermitemgerarrecomendações.Issoéinteressanteporque enfatiza a diversidade de opções; por outro lado, pode causar dúvida para o projetista do sistema sobre qual é a melhor técnica para usar. Cada uma destas abordagens tem particularidades e dependem do contexto para serem aplicadas. Assim, a decisão de escolher entre técnicas se torna complexa para ser feita manualmente. Este trabalho propõe uma abordagem evolutiva para automatizar a busca pela melhor combinação de resultados de técnicas de Sistemas de Recomendação e produzir menos erros nas recomendações.Paraavaliaraproposta,foramrealizadosexperimentoscomumconjunto de dados daMovieLens e algumas das técnicas de Filtragem Colaborativa. Os resultados mostramqueametodologiadecombinação,propostanestetrabalho,temumdesempenho melhor do que qualquer uma das técnicas isoladas de filtragem colaborativa no contexto abordado.A melhora varia de3,6%a 118,99%dependendo da técnica e do experimento executado

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities