Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems

Domingues, Marcos Aurélio; Manzato, Marcelo G.; Marcacini, Ricardo Marcondes; Sundermann, Camila Vaccari; Rezende, Solange Oliveira

doi:10.1109/icpr.2014.620

Cited by 14 publications

(11 citation statements)

References 21 publications

(33 reference statements)

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“…These techniques, in their traditional forms, consider only the set of ratings or user accesses. However, empirical studies indicate that context-aware approaches can produce more precise recommendations [10][11][12][13][14][15][16][17]. A travel package recommender system, for example, can improve the performance of the recommendation by considering the context "season of the year" in which the user wishes to travel since some places are recommended in "summer" while others are recommended in "winter".…”

Section: Introductionmentioning

confidence: 99%

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Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

et al. 2019

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Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

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Section: Introductionmentioning

confidence: 99%

“…In Table 3, we answer these research questions according to each reviewed paper. Fourteen (14) of the 17 research studies uses sentiment as the opinion information. However, only seven works apply opinion mining at the aspect level (see Table 1).…”

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confidence: 99%

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

et al. 2019

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“…An author must cite appropriate and relevant previous studies to assist readers to have in-depth knowledge of a particular research resource [14]. Also, context-aware recommendation technique [15] further enhances recommendation of research resources by modeling and predicting researcher's preferences through the introduction of contextual information (variable) into the recommendation process as an explicit additional class of data [16,17] information improves the prediction accuracy of recommendation systems and also increases the performance of recommendation systems [18,19]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Contextual Variables for Research Resources Recommender System

Isinkaye¹,

Folajimi²

2018

IJISA

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Abstract-Context-aware recommender system (CARS) is a promising technique for recommending research resources to users (researchers) by predicting their preferences (resources) under different situations. If the contextual information given to such a system is inappropriate, it will certainly have a negative effect on the nature of recommendation output generated by the system as well as making the system to have high dimensionality complexity. Currently, several CARS recommendation algorithms have been developed but they have failed to bring to bear the means and importance of experimentally validating the contextual information used in different domains of application of CARS. Hence, this paper experimentally validates the contextual variables in the domain of research resources by splitting a research resource (article) into three major sections (introduction, review and methodology). These sections are the contextual variables validated in order to authenticate their viability as context that could be used in recommending research resources based on the specific section of an article a researcher is interested in. The result of our experiment shows that irrespective of the domain of articles, journal articles have higher variability in their citations at introduction, very significant variability between the articles in the review and high variability in the methodology contextual variable respectively than the articles in the proceeding under the three contextual variables. This experiment shows that these three variables could be used as context .It also shows the percentage of splitting that could be used within journals and proceedings for context-aware research resources recommendations.

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“…For the current version of the platform, we proposed and implemented a context-aware recommender based on the Weight Post-Filtering (PoF) approach [6]. For this recommender, we first compute the distance from a user u to each point of interest p in a city by using the spherical law of cosines [9], since the distance is based on the latitude and longitude coordinates:…”

Section: Recommendersmentioning

confidence: 99%

A Platform for the Recommendation of Points of Interest in Brazilian Cities

Domingues

Santos

Hanada

et al. 2015

Proceedings of the 21st Brazilian Symposium on Multimedia and the Web

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The tourism sector in Brazil has grown considerably in recent years. Despite this growth, the sector still presents several problems such as the lack of information in Portuguese and in other languages for Brazilian and foreign tourists. The absence of information about tourist sites and ordinary services also affects individuals when settling in a new city, as it is the case when freshman students move to a new city to start their studies in a college or university. In this work, we propose an innovative vision of a context-aware platform for recommending points of interest in Brazilian cities, designed with mechanisms for collecting data from the web, for extracting points of interest and background information, and for learning context-aware recommendation models. The platform is accessed by a mobile application. To validate our proposal, we ran a case study where freshman students used the platform during their first months in a new city.

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Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems

Cited by 14 publications

References 21 publications

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review

Experimental Validation of Contextual Variables for Research Resources Recommender System

A Platform for the Recommendation of Points of Interest in Brazilian Cities

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