A Survey of State-of-the-art: Deep Learning Methods on Recommender System

Tilahun, Basiliyos; Awono, Charles; Batchakui, Bernabé

doi:10.5120/ijca2017913361

Cited by 27 publications

(8 citation statements)

References 14 publications

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“…The consumer feels unable to find the suitable product among the multitude of items offered on the online marketplace. To overcome this problem and help the consumer in decision-making, recommendation systems (RSs), proven to be a useful technology [4], have been developed. These systems reduce the number of options that the customer has to choose from by analysing the needs or behaviours.…”

Section: Contextmentioning

confidence: 99%

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A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Mensouri

Azmani

2022

Applied Computer Systems

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In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.

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Section: Contextmentioning

confidence: 99%

“…• Confidence measures the number of times items in B appear in transactions involving A [53]. The Confidence is calculated as shown in (4).…”

Section: Generating Association Rules By Applying Apriori Algorithmmentioning

confidence: 99%

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

Mensouri

Azmani

2022

Applied Computer Systems

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“…To deal with these challenges, deep neural networks extract the information from tags and process the information through multiple layers to retrieve more advanced and abstract data [41]. Authors in [42] have provided a very short survey of deep learning methods used in RSs. Zhang et al [43] present a comprehensive review of deep learning techniques based RSs.…”

Section: Deep Learning In Recommender Systemsmentioning

confidence: 99%

“…Moreover, deep learning has also opened the doors for improving the accuracy of social recommender systems. [19], [37], [44], [46], [52] 2016 17 [16], [21], [24], [25], [28], [36], [40], [41], [45], [47], [57], [60], [61], [69], [70], [65], [71] 2017 20 [17], [18], [20], [22], [26], [27], [38], [42], [43], [51], [53], [62]- [64], [66], [68], [72]- [74], [78] VI. CONCLUSION A voluminous research has been done and is also proceeding in recommender systems using deep learning.…”

Section: Miscellaneousmentioning

confidence: 99%

An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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“…Different from the traditional recommendation model, deep learning can collect nonlinear and important user project relationships and can use higher-level data to digitize complex abstract code. In the era of big data, evaluating the characteristics of a large amount of information to make choices is a tedious and time-consuming task [17]. One solution to alleviate this overload problem is to build a recommendation system that can process a large amount of data and support users' decision-making ability.…”

Section: Introductionmentioning

confidence: 99%

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

Wang

2022

Scientific Programming

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With the popularization of online education and the idea of learning anytime and anywhere, more and more learners search and learn courses of various disciplines through online learning platforms to meet their personal knowledge needs. With the increase of the number of courses, it is difficult for learners to find the courses they want quickly and accurately; that is, they encounter the problems of information overload and cognitive maze. Therefore, how to recommend personalized courses for learners according to their preferences has become one of the important problems that need to be solved urgently to improve the service quality of courses in online learning platforms. Therefore, in order to improve the accuracy of course recommendation, it is necessary to build an accurate and complete learner model. In order to improve the application effect of recommendation, this paper focuses on the recommendation method of emotional factors to improve the recommendation efficiency of learning resources. The traditional recommendation model is a method based on the user’s purchase behavior and historical information. However, in the emotional factors, the effect of traditional recommendation is limited. This paper proposes a recommendation method based on emotional factors, which may consider the emotional and psychological factors of scholars according to the learning content of learners. The experimental results show that the learner model incorporating learners’ affection can reflect learners’ preferences more accurately, and the use of deep neural factorization machine for curriculum recommendation can effectively improve the accuracy of curriculum recommendation.

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A Survey of State-of-the-art: Deep Learning Methods on Recommender System

Cited by 27 publications

References 14 publications

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

A New Marketing Recommendation System Using a Hybrid Approach to Generate Smart Offers

An Application-oriented Review of Deep Learning in Recommender Systems

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

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