A social recommender system using item asymmetric correlation

Dakhel, Arghavan Moradi; Malazi, Hadi Tabatabaee; Mahdavi, Mehregan

doi:10.1007/s10489-017-0973-5

Cited by 30 publications

(10 citation statements)

References 39 publications

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“…e data source in our experiment is part of the product data and user behavior data from Jing Dong Mall, which is the largest independent business-to-consumer e-commerce business in China by transaction volumes [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Datasets and Parameter Setupmentioning

confidence: 99%

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Xiang

Zhang

2020

Mathematical Problems in Engineering

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Since cross-border e-commerce involves the export and import of commodities, it is affected by many policies and regulations, resulting in some special requirements for the recommendation system, which makes the traditional collaborative filtering recommendation algorithm less effective for the cross-border e-commerce recommendation system. To address this issue, a simple yet effective cross-border e-commerce personalized recommendation is proposed in this paper, which integrates fuzzy association rule and complex preference into a recommendation model. Under the constraint of fuzzy association rules, a hybrid recommendation model based on user complex preference features is constructed to mine user preference features, and personalized commodities recommendation is realized according to user behavior preference. Compared with the traditional recommendation algorithm, the improved algorithm reduces the impact of data sparsity. The experiment also verifies that the improved fuzzy association rule algorithm has a better recommendation effect than the existing state-of-the-art recommendation models. The recommendation system proposed in this paper has better generalization and has the performance to be applied to real-life scenarios.

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Section: Datasets and Parameter Setupmentioning

confidence: 99%

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Xiang

Zhang

2020

Mathematical Problems in Engineering

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“…Content analysis is required to make a recommendation. [10] Hybrid approach It always provides predictions to content of recommendation. It improves the user preferences for suggesting items to users.…”

Section: Content-based Recommendationmentioning

confidence: 99%

“…[3], [7], [10] Moreover, it is open source and allows analyzing users' behaviors through their activities and their posts [20]. This allows us to enrich the user profile with his behavior (We can be inspired by [20]).…”

Section: Content-based Recommendationmentioning

confidence: 99%

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An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

Benkaddour¹,

Taghezout²,

Kaddour-Ahmed³

et al. 2018

IJIMAI

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In this paper, we propose a global architecture of a recommender tool, which represents a part of an existing collaborative platform. This tool provides diagnostic documents for industrial operators. The recommendation process considered here is composed of three steps: Collecting and filtering information; Prediction or recommendation step; evaluating and improvement. In this work, we focus on collecting and filtering step. We mainly use information result from collaborative sessions and documents describing solutions that are attributed to the complex diagnostic problems. The developed tool is based on collaborative filtering that operates on users' preferences and similar responses.

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“…Therefore, recommender systems have been proposed to help users find the contents that they want, such as research articles [8], Point-of-Interest [20,32], question [4] and music [23,24]. Existing recommendation methods include collaborative filtering-based recommendations [12,29], content-based recommendations [16], social network-based recommendations [13,1], and hybrid recommendation [7].…”

Section: Introductionmentioning

confidence: 99%

Time-aware sequence model for next-item recommendation

Wang

et al. 2020

Appl Intell

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The sequences of users' behaviors generally indicate their preferences, which can be used to improve next item prediction in sequential recommendation. Unfortunately, the users' behaviors may change over time, and it remains a great challenge to capture the user's dynamic preference directly from her/his recent behaviors sequence. Traditional methods such as Markov Chains, Recurrent Neural Networks, and Long Short-Term Memory (LSTM) Networks only consider the relative order of items in sequence, but ignore some important time information, such as the time interval and duration in the sequence. In this paper, we propose a novel sequential recommendation model, named Interval and Duration aware LSTM (IDLSTM), which leverages the interval and duration information to accurately model users' long-term and short-term preferences. In particular, the IDLSTM model incorporates the global context information of the sequence in the input layer to make better use of long-term memory. Furthermore, we also present an improved version of IDLSTM, namely IDLSTM with Embedding layer and Coupling input and output gates (IDLSTM-EC), which introduces the coupled input and forget

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A social recommender system using item asymmetric correlation

Cited by 30 publications

References 39 publications

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

Cross-Border E-Commerce Personalized Recommendation Based on Fuzzy Association Specifications Combined with Complex Preference Model

An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis

Time-aware sequence model for next-item recommendation

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