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Relevant bibliographies by topics / COLLABORATIVE FILTERING ALGORITHMS

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Academic literature on the topic 'COLLABORATIVE FILTERING ALGORITHMS'

Author: Grafiati

Published: 26 October 2023

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Journal articles on the topic "COLLABORATIVE FILTERING ALGORITHMS"

1

Ben Kharrat, Firas, Aymen Elkhleifi, and Rim Faiz. "Improving Collaborative Filtering Algorithms." International Journal of Knowledge Society Research 7, no. 3 (2016): 99–118. http://dx.doi.org/10.4018/ijksr.2016070107.

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This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approac

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2

Cacheda, Fidel, Víctor Carneiro, Diego Fernández, and Vreixo Formoso. "Comparison of collaborative filtering algorithms." ACM Transactions on the Web 5, no. 1 (2011): 1–33. http://dx.doi.org/10.1145/1921591.1921593.

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3

Zhou, Li Juan, Ming Sheng Xu, and Hai Jun Geng. "Improved Attack-Resistant Collaborative Filtering Algorithm." Key Engineering Materials 460-461 (January 2011): 439–44. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.439.

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Collaborative filtering is very effective in recommendation systems. But the recently researches have proved the collaborative filtering is significant vulnerable in the face of profile injection attacks. Profile injection attacks can be identified to some attack models. The attacker can easily bias the prediction of the system based on collaborative filtering algorithms. In this paper, an improved algorithm based on Singular Value Decomposition is proposed. Some dimensions are chosen by the improved algorithm to find capture latent relationships between customers and products. In addition, th

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4

Wu, Xinyi. "Comparison Between Collaborative Filtering and Content-Based Filtering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 480–89. http://dx.doi.org/10.54097/hset.v16i.2627.

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With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by de

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5

Jalili, Mahdi. "A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics." International Journal of System Modeling and Simulation 2, no. 2 (2017): 14. http://dx.doi.org/10.24178/ijsms.2017.2.2.14.

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Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items),

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6

Zhang, Zhen, Taile Peng, and Ke Shen. "Overview of Collaborative Filtering Recommendation Algorithms." IOP Conference Series: Earth and Environmental Science 440 (March 19, 2020): 022063. http://dx.doi.org/10.1088/1755-1315/440/2/022063.

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7

Jing, Hui. "Application of Improved K-Means Algorithm in Collaborative Recommendation System." Journal of Applied Mathematics 2022 (December 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/2213173.

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With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm

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Jiang, Tong Qiang, and Wei Lu. "Improved Slope One Algorithm Based on Time Weight." Applied Mechanics and Materials 347-350 (August 2013): 2365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2365.

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Collaborative filtering is regarded as the most prevailing techniques for recommendation system. Slope one is a family of algorithms used for collaborative filtering. It is the simplest form of non-trivial item-based collaborative filtering based on ratings. But all the family of use CF algorithms ignores one important problem: ratings produced at different times are weighted equally. It means that they cant catch users different attitudes at different time. So in this paper, we present a new algorithm, which could assign different weights for items at different time. Finally, we experimentall

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Li, Xiaofeng, and Dong Li. "An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy." Mobile Information Systems 2019 (May 7, 2019): 1–11. http://dx.doi.org/10.1155/2019/3560968.

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The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collabor

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10

Kourtiche, Ali, and Mohamed Merabet. "Collaborative Filtering Technical Comparison in Implicit Data." International Journal of Knowledge-Based Organizations 11, no. 4 (2021): 1–24. http://dx.doi.org/10.4018/ijkbo.2021100101.

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Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set b

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