Integrating DBpedia and SentiWordNet for a tourism recommender system

Varga, Bernadette; Groza, Adrian

doi:10.1109/iccp.2011.6047856

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The General Architecture for Text Engineering (GATE) 1 , a text processing tool, is adopted to extract the keywords from textual metadata automatically. Several applications in tourism have been built based on GATE, such as the cultural-tourist information system [53] tourism recommender system [54], and tourism web services [55].…”

Section: A Textual Metadata Processingmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

2020

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Understanding the interests of tourists is a key skill for attraction managers to prepare plans and make strategic decisions in tourism marketing. The rapid growth and spread of social media websites provide an information-rich channel from which tourism researchers and managers can collect a large amount of text-based reviews or comments and photos relating to the past travel experiences of users. The travel photos with geographic information are especially helpful in identifying the geographical location of the destinations. By analyzing these big data in various formats can help to understand the interests of tourists at destinations. In this paper, a framework is proposed to identify the interests of tourists by integrating information carried by the geotagged photos shared on social media websites. Such an approach is expected to provide sustainable tracking on popular places of interest (POIs) updated by tourists and pick the best representative photos taken by them. The performance of this model is evaluated by conducting a case study using the geotagged photos taken in Hong Kong. A case study proved this proposed framework could make a thriving tourism industry more efficient. INDEX TERMS representative photo, geotagged photo, tourist interest, tourist activity.

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Section: A Textual Metadata Processingmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

2020

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“…The main example of linked open data is DBPedia [10], a popular ontology used in recommender systems [44]. Such systems are used for recommender systems in several domains, including music [45] and tourism [46]. However, the methods to fuse similarity based on ontologies and other techniques do not go beyond simple score combination by using stacking [47].…”

Section: Related Resultsmentioning

confidence: 99%

Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors

Kelen

Daróczy

Ayala-Gómez

et al. 2019

Sensors

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Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics.

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“…By applying above formulae we obtained following matrix, where rows are populated with Items or subject s and columns [20], OpenLink Virtuoso [21], Fuseki [22], 4Store [23], StarDog [24]) are present that have the capability to store the RDF triples in efficient manner for quick consumption. For testing purpose we choose Sesame 2.7, that can work with windows system with 3GB RAM.…”

Section: Overall Weight Calculation Overall_weight = αP*itmlinps/1+logtmentioning

confidence: 99%

Utilizing DBpedia via CBR Approach of Recommender System

Kushwaha¹,

Mahule²,

Op³

2013

Int J Adv Innovat Thoughts Ideas

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Using Linked Open Data Cloud for knowledge extraction is a challenging as well as budding research field. Knowledge extraction is essential work of developing a Recommendation System (RS). Traditional context analyzers of Content Based Recommendation (CBR) are no more sufficient in current web era of semantics. This problem can be removed through generation of features from the logic of semantics presents implicitly in structured format in the web. This promising area merge various domains and technologies namely, semantic web, machine learning, personalization and information retrieval for achieving a good recommendation.This paper discussed about the research questions and challenges that originate from the extraction of semantic knowledge. This semantics can be accessed from a huge open source data cloud that are linked in meaningful way and named as Linked Open Data Cloud (LOD). The implementation includes key methods for how to gain semantically enriched data related to particular items. Paper explains the book domain residing inside the DBpedia datasets, which consist of cross domain information.

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Integrating DBpedia and SentiWordNet for a tourism recommender system

Cited by 6 publications

References 13 publications

Notice of Violation of IEEE Publication Principles: A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

Notice of Violation of IEEE Publication Principles: A Big Data Framework to Identify Tourist Interests Based on Geotagged Travel Photos

Session Recommendation via Recurrent Neural Networks over Fisher Embedding Vectors

Utilizing DBpedia via CBR Approach of Recommender System

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