Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Nassar, Nour; Jafar, Assef; Rahhal, Yasser

doi:10.1186/s40537-020-00309-6

Cited by 20 publications

(14 citation statements)

References 21 publications

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“…Further, they have modified their approach using matrix factorization (MF) along with deep neural network (DNN). In Reference 27, author used multi‐criteria ratings and feed them as an input to a fused model of a DNN and MF to predict the criteria ratings. Recently, Jhalani et al 18 proposed the multi linear regression approach for determining the weights for each criterion and calculating the overall ratings predictions of each item and the superiority of their approach in comparison to other.…”

Section: Literature Reviewmentioning

confidence: 99%

A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming

Gupta

Kant

2022

Concurrency and Computation

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Multi-criteria recommender systems (MCRSs) provide suggestions to users based on their preferences to various criteria. Incorporation of criteria ratings into recommendation framework can provide quality recommendations to users because these ratings can elicit users' preferences efficiently. However, elicitation of user's overall preference based on criteria ratings is a key issue in MCRS. Even though several aggregation methods for the elicitation of users' overall preference have been investigated in the literature, no method has been shown the superiority under all circumstances. Therefore, we propose a model based approach to user preference discovery in multi-criteria RS using genetic programming (GP). In this work, we suggest three-stage process to generate recommendations to users. First, we learn user preference transformation function to aggregate criteria ratings by using GP, and then we utilize the preference function, so derived, for computing similarities in MCRS. Finally, items are recommended to users.Experimental results on Yahoo! Movies dataset show the superiority of our proposed approach in comparison to other aggregation approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming

Gupta

Kant

2022

Concurrency and Computation

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“…The purpose of recommender systems is to help users conduct searches by suggesting resources (items) that best match their interests and preferences [11,12]. The use of recommender systems in Computer Environments for Human Learning (CEHL) aims to support learners in the learning process to achieve their learning goals [13].…”

Section: Recommender System To Support Learnersmentioning

confidence: 99%

Towards a folksonomy graph-based context-aware recommender system of annotated books

et al. 2021

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The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners’ opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.

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“…[12] presented a deep multi-criteria collaborative filtering model for RS that has two networks one for predicting the criteria ratings while the other for predicting the overall rating, then in [2], they fused matrix factorization with deep neural network in order to predict the criteria ratings.…”

Section: Related Workmentioning

confidence: 99%

“…These systems provide recommendations and suggestions for the options that are most appropriate for each person. Most RSs attract users' opinions about an item through a single criterion, which leads to performance deficiencies because when the user expresses his opinion on an item [2], he may enjoy some of the element's properties (such as Food) and do not enjoy other properties, such as restaurant service. As a result, RSs based on several criteria were created, in order to evaluate the various properties of an item; these systems are called Multi-Criteria Recommender Systems (MCRSs) [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

Nassar¹

2021

Preprint

Self Cite

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Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

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Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

Cited by 20 publications

References 21 publications

A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming

A model‐based approach to user preference discovery in multi‐criteria recommender system using genetic programming

Towards a folksonomy graph-based context-aware recommender system of annotated books

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

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