Sequencing of items in personalized recommendations using multiple recommendation techniques

Tewari, Ashish; Barman, Asim Gopal

doi:10.1016/j.eswa.2017.12.019

Cited by 34 publications

(23 citation statements)

References 12 publications

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“…However, existing recommendation methods bear limitations, including calculation complexity and information incompleteness (Asunur & Hulisi, ; Tewari & Barman, ; C. Zhang, Zhang, & Wang, ). To deal with aforementioned problems, two effective methods are developed under probabilistic linguistic circumstance, and the relative data are acquired from TripAdvisor.com.…”

Section: Illustrative Casementioning

confidence: 99%

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Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Wang

Zhang

2018

Expert Systems

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Probabilistic linguistic term sets (PLTSs) are an important expression for hesitant linguistic preference information under group decision‐making circumstances. This study investigates problems of multicriteria group decision making (MCGDM) with PLTSs. A novel and rational comparison method is first proposed, and two distance measures for PLTSs are defined. The weight of each criterion is then obtained via maximum deviation method. Subsequently, extended Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) ‐ VIseKriterijumska Optimizacija I Kompromisno Resenje a Serbian name (VIKOR) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) methods are developed as decision support models to handle MCGDM problems. An illustrative example is also analysed to demonstrate the rationality and feasibility of the proposed methods.

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Section: Illustrative Casementioning

confidence: 99%

“…However, existing recommendation methods bear limitations, including calculation complexity and information incompleteness (Asunur & Hulisi, 2016;Tewari & Barman, 2018;C. Zhang, Zhang, & Wang, 2018).…”

Section: Illustrative Casementioning

confidence: 99%

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Wang

Zhang

2018

Expert Systems

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“…Though there are many approaches exist for recommending items, they create a big list of recommendations for target users. Tewari and Barman [6] tackled the problem by combining the features of content based filtering, collaborative filtering, matrix factorization and opinion mining. Their approach proposed top-N recommendation where 'N' is small and have high precision value.…”

Section: Related Workmentioning

confidence: 99%

Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

Thillainayagam¹,

Kunjithapatham²,

Ramkumar³

2018

IJET

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In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer's pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of recommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been constructed and experimental investigations are carried out in a benchmark dataset.

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“…But it will produce the same results for all users so that the resulting personalization is still lacking. In addition, the use of opinion mining methods for rating calculations in research [11] also cannot be used in general datasets. This is because the opinion mining method (prediction rating) used gives a fairly good result only on the data with short comments/reviews such as on the website myopinions.in.…”

Section: Introductionmentioning

confidence: 99%

Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

Ghifari

Sitohang

Saptawati

2021

ITJRD

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Increasing number of internet users today, the use of e-commerce becomes a very vital need. One of the keys that holds the success of the e-commerce system is the recommendation system. Collaborative filtering is the popular method of recommendation system. However, collaborative filtering still has issues including data sparsity, cold start, gray sheep, and dynamic taste. Some studies try to solve the issue with hybrid methods that use a combination of several techniques. One of the studies tried to solve the problem by building 7 blocks of hybrid techniques with various approaches. However, the study still has some problems left. In the case of cold start new users, actually, the method in the study has handled it with matrix factorizer block and item weight. But it will produce the same results for all users so that the resulting personalization is still lacking. This study aims to map an overview of the themes of recommendation system research that utilizes bibliometric analysis to assess the performance of scientific articles while exposing solution opportunities to cold start problems in the recommendation system. The results of the analysis showed that cold start problems can be solved by utilizing social network data and graph approaches.

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Sequencing of items in personalized recommendations using multiple recommendation techniques

Cited by 34 publications

References 12 publications

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Distance‐based multicriteria group decision‐making approach with probabilistic linguistic term sets

Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

Addressing Cold Start New User in Recommender System Based on Hybrid Approach: A review and bibliometric analysis

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