Browsing by Author "Burguillo, Juan Carlos"
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- A 2020 perspective on “Online guest profiling and hotel recommendation”Publication . Veloso, Bruno M.; Leal, Fátima; Malheiro, Benedita; Burguillo, Juan CarlosTourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.
- Analysis and prediction of hotel ratings from crowdsourced dataPublication . Leal, Fátima; Malheiro, Benedita; Burguillo, Juan CarlosCrowdsourcing has become an essential source of information for tourism stakeholders. Every day, tourists leave large volumes of feedback data in the form of posts, likes, textual reviews, and ratings in dedicated crowdsourcing platforms. This behavior makes the analysis of crowdsourced information strategic, allowing the discovery of important knowledge regarding tourists and tourism resources. This paper presents a survey on the analysis and prediction of hotel ratings from crowdsourced data, covering both off‐line (batch) and on‐line (stream‐based) processing. Specifically, it reports multiple rating‐based profiling, recommendation, and evaluation techniques. While most of the surveyed works adopt entity‐based multicriteria profiling, prerecommendation filtering, and off‐line processing, the latest hotel rating prediction trends include feature‐based, trust and reputation modeling, postrecommendation filtering, and on‐line processing. Additionally, since the volume of crowdsourced ratings tends to increase, the deployment of profiling and recommendation algorithms on high‐performance computing resources should be further explored.
- Analysis and Visualisation of Crowd-sourced Tourism DataPublication . Leal, Fátima; Dias, Joana Matos; Malheiro, Benedita; Burguillo, Juan CarlosThe tourist behaviour has changed significantly over the last decades due to technological advancement (e.g., ubiquitous access to the Web) and Web 2.0 approaches (e.g., Crowdsourcing). Tourism Crowdsourcing includes experience sharing in the form of ratings and reviews (evaluation-based), pages (wiki-based), likes, posts, images or videos (social-network-based). The main contribution of this paper is a tourist-centred off-line and on-line analysis, using hotel ratings and reviews, to discover and present relevant trends and patterns to tourists and businesses. On the one hand, online, we provide a list of the top ten hotels, according to the user query, ordered by the overall rating, price and the ratio between the positive and negative Word Clouds reviews. On the other hand, off-line, we apply Multiple Linear Regression to identify the most relevant ratings that influence the hotel overall rating, and generate hotel clusters based on these ratings.
- B2B platform for media content personalisationPublication . Malheiro, Benedita; Foss, Jeremy; Burguillo, Juan CarlosThis paper proposes a novel business model to support media content personalisation: an agent-based business-to-business (B2B) brokerage platform for media content producer and distributor businesses. Distributors aim to provide viewers with a personalised content experience and producers wish to en-sure that their media objects are watched by as many targeted viewers as possible. In this scenario viewers and media objects (main programmes and candidate objects for insertion) have profiles and, in the case of main programme objects, are annotated with placeholders representing personalisation opportunities, i.e., locations for insertion of personalised media objects. The MultiMedia Brokerage (MMB) platform is a multiagent multilayered brokerage composed by agents that act as sellers and buyers of viewer stream timeslots and/or media objects on behalf of the registered businesses. These agents engage in negotiations to select the media objects that best match the current programme and viewer profiles.
- Brokerage Platform for Media Content RecommendationPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan CarlosNear real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.
- Collaborative Filtering with Semantic Neighbour DiscoveryPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan CarlosNearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.
- Context-aware tourism technologiesPublication . Leal, Fátima; Malheiro, Benedita; Burguillo, Juan CarlosNowadays travellers can benefit from the computing capabilities, collection of on board sensors and ubiquitous Internet access provided by mobile devices. These are the three pillars of any tourist support system since they provide the power, means and data to establish the local user context, to access remote services and to provide value-added user-centred context-aware applications. However, making sense of the user context data is not straightforward, as it requires dedicated knowledge acquisition and knowledge representation solutions. Besides, the range and diversity of available data sources is huge, requiring appropriate knowledge processing techniques to provide addequated tourism services. This article presents an updated review, and a comparison of recent context-aware tourism applications (CATA), including supporting technologies; and considering four possible dimensions: knowledge acquisition, knowledge representation, knowledge processing and knowledge-based services. We propose and apply a CATA analysis framework, contemplating these four dimensions to the applications found in the literature. This survey constitutes, not only, a state of the art review on tourism mobile applications, but, also, anticipates the latest development trends in tourism-related applications.
- Dynamic Personalisation of Media ContentPublication . Malheiro, Benedita; Foss, Jeremy; Burguillo, Juan Carlos; Peleteiro, Ana; Mikic, Fernando A.Dynamic personalization of media content is the latest challenge for media content producers and distributors. The idea is to adapt in near real time the content of a video stream to the viewer's profile. This concept encompasses any type of context-awareness customisation, expressed preferences and viewer profiling. To achieve this goal we propose a multi tier framework composed of a content production tier, a content distribution tier and a content consumption tier, representing producers, distributors and viewers, plus an artefact brokerage tier, implemented as an agent-based e-brokerage platform, to support the dynamic selection of the content to be inserted in the video stream of each viewer.
- Federated IaaS Resource BrokeragePublication . Veloso, Bruno; Meireles, Fernando; Malheiro, Benedita; Burguillo, Juan CarlosThis paper presents the CloudAnchor brokerage platform for the transaction of single provider as well as federated Infrastructure as a Service (IaaS) resources. The platform, which is a layered Multi-Agent System (MAS), provides multiple services, including (consumer or provider) business registration and deregistration, provider coalition creation and termination, provider lookup and invitation and negotiation services regarding brokerage, coalitions and resources. Providers, consumers and virtual providers, representing provider coalitions, are modelled by dedicated agents within the platform. The main goal of the platform is to negotiate and establish Service Level Agreements (SLA). In particular, the platform contemplates the establishment of brokerage SLA – bSLA – between the platform and each provider or consumer, coalition SLA – cSLA – between the members of a coalition of providers and resource SLA – rSLA – between a consumer and a provider. Federated resources are detained and negotiated by virtual providers on behalf of the corresponding coalitions of providers.
- Impact of Trust and Reputation Based Brokerage on the CloudAnchor PlatformPublication . Veloso, Bruno; Malheiro, Benedita; Burguillo, Juan Carlos; Gama, JoãoThis paper analyses the impact of trust and reputation modelling on CloudAnchor, a business-to-business brokerage platform for the transaction of single and federated resources on behalf of Small and Medium Sized Enterprises (SME). In CloudAnchor, businesses act as providers or consumers of Infrastructure as a Service (IaaS) resources. The platform adopts a multi-layered multi-agent architecture, where providers, consumers and virtual providers, representing provider coalitions, engage in trust & reputation-based provider look-up, invitation, acceptance and resource negotiations. The goal of this work is to assess the relevance of the distributed trust model and centralised fuzzified reputation service in the number of resources successfully transacted, the global turnover, brokerage fees, losses, expenses and time response. The results show that trust and reputation based brokerage has a positive impact on the CloudAnchor performance by reducing losses and the execution time for the provision of both single and federated resources and increasing considerably the number of federated resources provided.
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