A collaborative filtering method based on artificial immune network

Acilar, Ayşe Merve; Arslan, Ahmet

doi:10.1016/j.eswa.2008.10.029

Cited by 80 publications

(8 citation statements)

References 19 publications

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“…System users then have a profile that describes their areas of interest. Each user's profile can contain a list of topics or preferences [8], [9]. The system compares the description of a new arrived document with the user's profile to predict the usefulness of that document for that user [10].…”

Section: Recommandation System Approches 41 Content-based Filteringmentioning

confidence: 99%

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Hssina

Grota

Erritali

2021

IJECE

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<span>Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.</span>

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Section: Recommandation System Approches 41 Content-based Filteringmentioning

confidence: 99%

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Hssina

Grota

Erritali

2021

IJECE

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“…This approach recommends Hotels or products by finding similar users to these hotels. It recommends hotels to you or the user based on their ranking and opinion [6][7][8]. Ringo is an online system that uses collaborative filtering (CF) approach and rating of the users on music albums to construct a user profile [9].…”

Section: B Similarity Measurementsmentioning

confidence: 99%

Hotel Recommender System based on Knowledge Graph and Collaborative Approach

Abuzir¹,

Dwieb²

2021

IJC

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With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.

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“…Therefore, the main focus of this paper is to examine solutions for user cold-start rather than item cold-start problem. The user cold-start problem [1,20] refers to the challenge of making recommendations for users with little to no prior history with the service or medium, or for items that have little historical interaction to draw on. It is an important problem because cold-start recommendations often form a user's first impression of a recommendation service; if they are poor, the user may be permanently put off.…”

Section: Cold-start Recommendationsmentioning

confidence: 99%

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Nazari

Charbuillet

Pages

et al. 2020

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.

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A collaborative filtering method based on artificial immune network

Cited by 80 publications

References 19 publications

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Hotel Recommender System based on Knowledge Graph and Collaborative Approach

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

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