Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques

Yadav, Pratibha; Tyagi, Shweta

doi:10.1504/ijcse.2017.087413

Cited by 5 publications

(5 citation statements)

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“…For prediction of item by user , User-based prediction and Item-based predictions are represented by ( ) and ( ) respectively. Moreover, ratings are also predicted using the Hybrid approach as suggested by Yadav and Tyagi [24]. The prediction of the rating of item by user using a Hybrid scheme, ( ), is given by the following formula Equation 11.…”

Section: 33recommendation Of Itemsmentioning

confidence: 99%

“…The parameter λ decides the consequences of user based and item-based approaches on the hybrid recommendation model [24]. It ranges from 0 to 1, where 0 indicates that final predictions wholly rely on user-based model whereas value 1 specifies that recommendations are based entirely on an Item-based approach.…”

Section: 53impact Of λmentioning

confidence: 99%

“…A learned user behavior model is then employed to anticipate the active user's behavior [23]. Hybrid CF recommendation systems combine CF approaches with additional recommendation techniques to analyse and predict user behaviour [23,24]. CF techniques performance is adversely affected by the issues such as sparsity, scalability, and cold-start [17,25].…”

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confidence: 99%

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Evolutionary extreme learning machine based collaborative filtering

2023

IJATEE

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Since their emergence in the middle of the 1990s, recommender systems (RSs) have drawn more interest from academics and businesses [1,2]. These systems investigate and assess user data from social media platforms, including followers, followed, comments, likes, dislikes, tweets, posts, and more [3,4]. Additionally, the Internet of Things (IoT) has developed into a new source of information that includes global positioning system (GPS) coordinates, radio frequency identification (RFID) data, health indicators [5] surveillance data, etc.To provide consumers, with the suggestions or recommendations they want, RS uses a variety of information sources, opening a popular field for additional study and improvement [3,4].

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Section: 33recommendation Of Itemsmentioning

confidence: 99%

Section: 53impact Of λmentioning

confidence: 99%

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Evolutionary extreme learning machine based collaborative filtering

2023

IJATEE

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“…Fuzzy collaborative clustering is one of the main streams in fuzzy collaborative intelligence [15][16]20,[22][23][24]. Pedrycz [25] defined the concept of granular fuzzy modelling as the application of fuzzy collaborative clustering methods to information granules.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yadav and Tyagi [23] proposed a fuzzy collaborative clustering method for recommending items to customers, in which fuzzy c-means (FCM) was applied to generate item and customer clusters. A weighting scheme was then used to aggregate the two types of clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

An Auto-Weighting Aggregative Fuzzy Collaborative Intelligence Approach for DRAM Yield Forecasting

Wu¹,

Chen²

2021

Preprint

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In a collaborative forecasting task, experts may have unequal authority levels. However, this has rarely been considered reasonably in the existing fuzzy collaborative forecasting methods. In addition, experts may not be willing to discriminate their authority levels. To address these issues, an auto-weighting fuzzy weighted intersection (FWI) fuzzy collaborative intelligence approach is proposed in this study. In the proposed auto-weighting FWI fuzzy collaborative intelligence approach, experts’ authority levels are automatically and reasonably assigned based on their past forecasting performances. Subsequently, the auto-weighting FWI mechanism is established to aggregate experts’ fuzzy forecasts. The theoretical properties of the auto-weighting FWI mechanism have been discussed and compared with those of the existing fuzzy aggregation operators. After applying the auto-weighting FWI fuzzy collaborative intelligence approach to a case of forecasting the yield of a DRAM product from the literature, its advantages over several existing methods were clearly illustrated.

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