Incorporating Multi-Criteria Ratings in Recommendation Systems

Lee, Hsin-Hsien; Teng, Wei-Guang

doi:10.1109/iri.2007.4296633

Cited by 36 publications

(16 citation statements)

References 9 publications

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“…This related work contemplates: (1) multi-criteria tourism crowdsourced ratings in hotel recommendation systems; (2) collaborative filtering; and (3) trust-based modelling. Adomavicius and Kwon [2], Bilge and Kaleli [4], Lee and Teng [24], Jhalani et al [17], Liu et al [26], Manouselis and Costopoulou [27] and Shambour et al [32] have explored the integration of multi-criteria ratings in the user profile, mainly using multimedia datasets to validate their proposals. Davoudi et al [7], Jia et al [18] and Zhang et al [37] have explored the trust modelling for rating prediction presenting trust models together with matrix factorisation algorithms or similarity metrics.…”

Section: Related Workmentioning

confidence: 99%

Trust-based Modelling of Multi-criteria Crowdsourced Data

et al. 2017

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As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.

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Section: Related Workmentioning

confidence: 99%

Trust-based Modelling of Multi-criteria Crowdsourced Data

et al. 2017

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“…Thus, profiling and prediction using tourism crowd-sourced multi-criteria ratings is an important research topic for the hospitality industry. Adomavicius and Kwon (2015), Bilge and Kaleli (2014), Lee and Teng (2007), Jhalani et al (2016), Liu et al (2011), Manouselis and Costopoulou (2007), and Shambour et al (2016) have explored the integration of multi-criteria ratings in the user profile, mainly using multimedia data sets to validate their proposals. However, scant research considers crowd-sourced multi-criteria ratings for profiling and rating prediction applied to the tourism domain.…”

Section: Related Workmentioning

confidence: 99%

Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings

Leal¹,

González–Vélez²,

Malheiro³

et al. 2017

ECMS 2017 Proceedings Edited by Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, Ján

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Based on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating -single criterion (SC) profiling -or on the multiple ratings available -multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (ı) the selection of the most representative crowd-sourced rating; and (ıı) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.

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“…Some other recommendation approaches described in the literature use multi-objective optimization methods by combining multiple criteria, e.g. [35][36][37][38] and [16]. Even though these works use multiple features at once, none of them uses data from multiple sources.…”

Section: Related Workmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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Incorporating Multi-Criteria Ratings in Recommendation Systems

Cited by 36 publications

References 9 publications

Trust-based Modelling of Multi-criteria Crowdsourced Data

Trust-based Modelling of Multi-criteria Crowdsourced Data

Profiling And Rating Prediction From Multi-Criteria Crowd-Sourced Hotel Ratings

Making recommendations by integrating information from multiple social networks

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