Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network

Afoudi, Yassine; Lazaar, Mohamed; Achhab, Mohammed Al

doi:10.1016/j.simpat.2021.102375

Cited by 93 publications

(39 citation statements)

References 13 publications

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“…Answering question Q4 defined in Table 1, if we focus on the different filtering methods used by the analyzed systems, we can see a tendency in the use of the collaborative filter. This is not a common result; normally content-based filters are used as starting filtering methods [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47], and once a considerable amount of data from the users are obtained, collaborative filters are launched. This might be because some studies were started with some initial data about the users.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Nutritional Recommendation Systems

2021

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

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Section: Discussionmentioning

confidence: 99%

Systematic Review of Nutritional Recommendation Systems

2021

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“…Despite the noticeable decline in their popularity in favor of collaborative systems, content-based techniques are still widely used because of handling the so-called cold-start problem [10]. Because of the significantly different characteristics of those approaches, it is advisable to construct hybridizations of both [11], as further discussed in our study.…”

Section: Related Workmentioning

confidence: 94%

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

Kannout¹,

Grodzki²,

Grzegorowski³

2022

Annals of Computer Science and Information Systems

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Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) -a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent itemsets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.Index Terms-recommendation system, cold-start problem, frequent pattern mining, quality of recommendations.

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“…A recent study of Afoudi et al [37] created a hybrid recommender system that combines collaborative filtering, content based similarity and a special type of neural network. The authors used the hybrid system in an unsupervised data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Algorithmic Similarity Measure for Collaborative Filtering: A Recommendation System Based on Rating Distances

Birim

TÜMTÜRK

2022

Academic Platform Journal of Engineering and Smart Systems

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Internet sources contain a vast amount of information about items that people desire to purchase. It is impossible to evaluate these resources and come to an informed decision. People need automated systems that evaluate previous information and propose item alternatives. Recommending items using a smart system, which is based on the previous user preferences, has growing importance since the available product data is exponentially growing. Additionally, it is difficult to find new and correct things that a user would like among this massive amount of data. To make accurate recommendations with a smart system, researchers and practitioners use collaborative filtering methods with similarity calculation based on user preferences. The crucial point in collaborative filtering is to find a valuable measure that resembles correct similarity between users. The current similarity metrics in the literature have some disadvantages in conducting accurate recommendations. To improve the recommendation performance, this study proposes a novel similarity measure that assesses the distance between the user's ratings and the median score. Considering distance from the median score is essential since some users may prefer to rate close to the median rather than the extremes. Experiments were conducted with a famous collaborative filtering dataset. Results showed that proposed similarity measure demonstrated superior performance regarding the recommendation accuracy. Implications of our results for XYZ are discussed.

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Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network

Cited by 93 publications

References 13 publications

Systematic Review of Nutritional Recommendation Systems

Systematic Review of Nutritional Recommendation Systems

Utilizing Frequent Pattern Mining for Solving Cold-Start Problem in Recommender Systems

A Novel Algorithmic Similarity Measure for Collaborative Filtering: A Recommendation System Based on Rating Distances

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