Results 31 to 40 of about 201,081 (282)
Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets
Big Data and Cognitive Computing, 2023 Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values ...
Dionisis Margaris +3 more
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Optimization of English Learning Platform Based on a Collaborative Filtering Algorithm
Complexity, 2021 This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific ...
Jiali Tang
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Online Collaborative-Filtering on Graphs [PDF]
SSRN Electronic Journal, 2013 Existing approaches to designing recommendation systems with user feedback focus on settings where the number of items is small and/or admit some underlying structure. It is unclear, however, if these approaches extend to applications like social network news feeds and content-curation platforms, which have large and unstructured content pools and ...
Siddhartha Banerjee +2 more
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Collaborative Filtering Recommendation Algorithm Based on Representation Learning of Knowledge Graph [PDF]
Jisuanji gongcheng, 2018 To solve the problem that collaborative filtering algorithm only uses the items-users rating matrix and does not consider semantic,a collaborative filtering recommendation algorithm is presented.Using the knowledge map to represent the learning method ...
WU Xiyu,CHEN Qimai,LIU Hai,HE Chaobo
doaj
Cybernetics and Information Technologies, 2023 One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions.
Amutha S., Vikram Surya R.
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Proceedings of the 12th ACM Conference on Recommender Systems, 2018 Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable efforts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of ...
Tiago Cunha 0001 +2 more
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Multi-space Interactive Collaborative Filtering Recommendation [PDF]
Jisuanji kexue, 2021 In the era of big data,due to information overload,it is difficult for users to find interesting content from massive data.The birth of personalized recommendation system has greatly solved this problem.Collaborative filtering has been widely used in the
LI Kang-lin, GU Tian-long, BIN Chen-zhong
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Rating Proportion-Aware Binomial Matrix Factorization for Collaborative Filtering
IEEE Access, 2023 Addressing biases in observed data is a major challenge in statistical and machine learning applications. This challenge also exists in recommendation systems, and various methods based on causal inference are being investigated.
Iwao Tanuma, Tomoko Matsui
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Deep social collaborative filtering [PDF]
Proceedings of the 13th ACM Conference on Recommender Systems, 2019 Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand ...
Wenqi Fan +5 more
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Collaborative Filtering with Stability
CoRR, 2018 Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce challenges to the algorithm ...
Dongsheng Li 0002 +5 more
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