570 research outputs found
Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing
Tourism crowdsourcing platforms have a profound influence
on the tourist behaviour particularly in terms of travel planning. Not
only they hold the opinions shared by other tourists concerning tourism
resources, but, with the help of recommendation engines, are the pillar
of personalised resource recommendation. However, since prospective
tourists are unaware of the trustworthiness or reputation of crowd publishers,
they are in fact taking a leap of faith when then rely on the
crowd wisdom. In this paper, we argue that modelling publisher Trust &
Reputation improves the quality of the tourism recommendations supported
by crowdsourced information. Therefore, we present a tourism
recommendation system which integrates: (i) user profiling using the
multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the
user ratings; (iii) Trust & Reputation modelling; and (iv) incremental
model update, i.e., providing near real-time recommendations. In terms
of contributions, this paper provides two different Trust & Reputation
approaches: (i) general reputation employing the pairwise trust values
using all users; and (ii) neighbour-based reputation employing the pairwise
trust values of the common neighbours. The proposed method was
experimented using crowdsourced datasets from Expedia and TripAdvisor
platforms.info:eu-repo/semantics/publishedVersio
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
Explanation plug-in for stream-based collaborative filtering
Collaborative filtering is a widely used recommendation technique, which often relies on rating information shared by users, i.e., crowdsourced data. These filters rely on predictive algorithms, such as, memory or model based predictors, to build direct or latent user and item profiles from crowdsourced data. To predict unknown ratings, memory-based approaches rely on the similarity between users or items, whereas model-based mechanisms explore user and item latent profiles. However, many of these filters are opaque by design, leaving users with unexplained recommendations. To overcome this drawback, this paper introduces Explug, a local model-agnostic plug-in that works alongside stream-based collaborative filters to reorder and explain recommendations. The explanations are based on incremental user Trust & Reputation profiling and co-rater relationships. Experiments performed with crowdsourced data from TripAdvisor show that Explug explains and improves the quality of stream-based collaborative filter recommendations.Xunta de Galicia | Ref. ED481B-2021-118Fundação para a Ciência e a Tecnologia | Ref. UIDB/50014/202
Why We Need New Evaluation Metrics for NLG
The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
this paper, we motivate the need for novel, system- and data-independent
automatic evaluation methods: We investigate a wide range of metrics, including
state-of-the-art word-based and novel grammar-based ones, and demonstrate that
they only weakly reflect human judgements of system outputs as generated by
data-driven, end-to-end NLG. We also show that metric performance is data- and
system-specific. Nevertheless, our results also suggest that automatic metrics
perform reliably at system-level and can support system development by finding
cases where a system performs poorly.Comment: accepted to EMNLP 201
Accessible POI Recommendation Using Adaptive Aggregation of Binary Ratings
Everyone needs one or more forms of accessibility at some point in life due to age, medical conditions, accidents, etc. People with accessibility needs have the right to accessible services, as well as the right to information about accessibility at various places or Points of Interest (POI). While most popular POI recommendation services do not take accessibility into account, some of them only consider a few specific needs, such as ramp for wheelchair users. However, different users have different accessibility needs regarding the structure of the building, special aid devices, and facilities to be able to independently visit a place. The proposed system focuses on finding the personalized accessibility score for a (user, POI) pair. It can be used with other factors such as historical behavior, social influence, geographical conditions, etc. to recommend accessible places. It uses time decaying aggregate on the crowd-sourced binary rating data to find accurate approximation of current accessibility status for each accessibility criteria. Also, we propose a tunnel-based algorithm to detect the trend of binary stream data to update the rate of decay. This ensures that the calculated aggregate adapts to change in the accessibility status of the place
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