Personalised Fuzzy Recommendation for High Involvement Products

Gerogiannis, Vassilis C.; Karageorgos, Anthony; Liu, Liwei; Tjortjis, Christos

doi:10.1109/smc.2013.831

Cited by 8 publications

(6 citation statements)

References 31 publications

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“…In this direction, some early works enriched the typical content-based recommendation scheme by incorporating fuzzy linguistic variables for modelling the preferences [79], subsequently considering more flexible frameworks for capturing the uncertainty of such user's preferences [80], even considering incomplete information [84]. Additionally, such sophisticated fuzzy linguistic approaches have also supported the construction of systems for recommending diverse items such as research resources [100,115], or furniture products [42].…”

Section: Proposalsmentioning

confidence: 99%

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Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

142

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Recommender systems are currently successful solutions for facilitating access for online users to the information that fits their preferences and needs in overloaded search spaces. In the last years several methodologies have been developed to improve their performance. This paper is focused on developing a review on the use of fuzzy tools in recommender systems, for detecting the more common research topics and also the research gaps, in order to suggest future research lines for boosting the current developments in fuzzy-based recommender systems. Specifically, it is developed an analysis of the papers focused at such aim, indexed in Thomson Reuters Web of Science database, in terms of they key features, evaluation strategies, datasets employed, and application areas.

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Section: Proposalsmentioning

confidence: 99%

“…Movielens, a non public dataset Business partner recommendation Gerogiannis et al [42] Helping buyers of high involvement products with the purchasing process, supported by fuzzy information modeling (2-tuple)…”

Section: Movielens Moviementioning

confidence: 99%

Fuzzy Tools in Recommender Systems: A Survey

Toledo¹,

Martínez²

2017

IJCIS

142

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“…According to the performance of each scheme under different criteria, it carries out a comprehensive evaluation, and then selects the best scheme from the limited schemes. Most of the project decision-making problems in our life can be modeled as multi-criteria decision making problems [1][2][3][4][5]. Because of its universality and importance, the exploration of such problems has shown progress.…”

Section: Introductionmentioning

confidence: 99%

Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Sun

Deng

2020

Mathematics

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Multi-criteria decision making (MCDM) refers to the decision making in the limited or infinite set of conflicting schemes. At present, the general method is to obtain the weight coefficients of each scheme based on different criteria through the expert questionnaire survey, and then use the Dempster–Shafer Evidence Theory (D-S theory) to model all schemes into a complete identification framework to generate the corresponding basic probability assignment (BPA). The scheme with the highest belief value is then chosen. In the above process, using different methods to determine the weight coefficient will have different effects on the final selection of alternatives. To reduce the uncertainty caused by subjectively determining the weight coefficients of different criteria and further improve the level of multi-criteria decision-making, this paper combines negation of probability distribution with evidence theory and proposes a weights-determining method in MCDM based on negation of probability distribution. Through the quantitative evaluation of the fuzzy degree of the criterion, the uncertainty caused by human subjective factors is reduced, and the subjective error is corrected to a certain extent.

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“…Recommended items can potentially be anything that a user is looking for, such as products, movies, songs, services etc. [25][26][27]. E-commerce is a domain that RS are used extensively [1,2].…”

Section: Introductionmentioning

confidence: 99%

Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

Beleveslis

Tjortjis

2020

IFIP Advances in Information and Communication Technology

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This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary "next step". Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights.

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Personalised Fuzzy Recommendation for High Involvement Products

Cited by 8 publications

References 31 publications

Fuzzy Tools in Recommender Systems: A Survey

Fuzzy Tools in Recommender Systems: A Survey

Determining Weights in Multi-Criteria Decision Making Based on Negation of Probability Distribution under Uncertain Environment

Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

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