A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS

Nilashi, Mehrbakhsh; Ibrahim, Othman; Ithnin, Norafida; Sarmin, Nor Haniza

doi:10.1016/j.elerap.2015.08.004

Cited by 182 publications

(74 citation statements)

References 60 publications

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“…The main goal is to refine guest and hotel profiling by reusing the multiple hotel ratings each guest shares over time, using data streams and computing the trustworthiness. According to Nilashi et al [28] and Adomavicius and Kwon [2], collaborative filtering with multi-criteria item ratings has been unexplored when compared with its single criterion item rating counterpart.…”

Section: Contributionsmentioning

confidence: 99%

“…Nilashi et al [28] propose a SC profiling approach together with a hybrid hotel recommendation model for multi-criteria recommendation. They employed: (1) principal component analysis (PCA) for the selection of the most representative rating (dimensionality reduction); (2) expectation maximisation (EM) and adaptive neuro-fuzzy inference system (ANFIS) as prediction techniques; and (3) TripAdvisor data for evaluation.…”

Section: Related Workmentioning

confidence: 99%

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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: Contributionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Trust-based Modelling of Multi-criteria Crowdsourced Data

et al. 2017

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“…Since the development of the first recommendation system by Goldberg and his colleagues [19], various recommendation systems and related technologies have been introduced. Among these systems, user-based CF [20] is the most popular in e-commerce, such as on Amazon.com [21]. This system identifies a customer's preference to recommend the Symmetry 2017, 9, 216 3 of 17 products most likely to be purchased by a similar customer group.…”

Section: The Recommendation System and Hybrid Algorithmmentioning

confidence: 99%

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

Guo

Wang

2017

Symmetry

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Abstract:With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer's feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.

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“…RS is uses by many areas in web such as movies [5], music [6], news [7], tourism [8], social networks [9] and scientific papers [10]. The main approaches of the recommenders includes content based and collaborative filtering where the first one predicts the interests of the user based on the sole user's data whereas the later one predicts the interests of the user based on the similar user's data [11].…”

Section: Related Workmentioning

confidence: 99%

DBT Recommender: Improved Trustworthiness of Ratings through De-Biasing Tendency of Users

Mukkamula¹

2018

IJIES

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Recent advancements in business strategies marked the significance of e-commerce in marketing any service of the organization. Moreover, users are quiet dependent on the average ratings of the products showcased in the marketing interface in turn these average ratings made remarkable impact on sales phenomena of the product. The average rating of a product is the aggregation of individual users ratings biased with the tendency of the user towards publishing the opinion. The optimistic user tends to give a slight high rating than a neutral judgement and vice versa with a pessimistic user. However, these biased ratings produce an aggregate value that is degraded with its trustworthiness. This paper proposed a novel approach named DBT (De-biased Tendency) Recommender to analyze the bias in product rating which recalculates the average ratings of the products by making user tendencies as part of the process. The solution implemented on a big data environment on demand of high computation complexity involved in the process. Experimental results had shown a significant improvement in the trustworthiness of the product ratings with the proposed approach.

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A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS

Cited by 182 publications

References 60 publications

Trust-based Modelling of Multi-criteria Crowdsourced Data

Trust-based Modelling of Multi-criteria Crowdsourced Data

An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model

DBT Recommender: Improved Trustworthiness of Ratings through De-Biasing Tendency of Users

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