Results 11 to 20 of about 201,081 (282)
On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets
One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item.
Dimitris Spiliotopoulos +2 more
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Interactive collaborative filtering [PDF]
In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback. Whilst users enjoy sequential recommendations, the recommendation predictions are constantly refined using up-to-date feedback on the recommended items ...
Xiaoxue Zhao +2 more
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Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies.
Aaron Ling Chi Yi, Dae-Ki Kang
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Application of Improved K-Means Algorithm in Collaborative Recommendation System
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
Hui Jing
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Structured collaborative filtering [PDF]
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '11 Proceedings of the 20th ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/10.1145/2063576.2063940.
Alejandro Bellogín +2 more
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Collaborative Filtering Algorithm Combining Ontology Semantics and User Attribute [PDF]
When dealing with massive data,the traditional collaborative filtering recommendation algorithm has the data sparsity and the long tail effect of the items,resulting in low recommendation accuracy.Aiming at this problem,combining ontology semantics and ...
WANG Guang, JIANG Li, DONG Shuaihan, LI Feng
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On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets
The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value.
Dionisis Margaris +2 more
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Perceptron Collaborative Filtering
Abstract: While multivariate logistic regression classifiers are a great way of implementing collaborative filtering - a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many other users, we can also achieve similar results using neural networks.
openaire +2 more sources
The intellectual system of movies recommendations based on the collaborative filtering
The investigation deals with designing and developing of intellectual system of movies recommendations based on the collaborative filtering using the Python software environment.
Stepan Sitkar +7 more
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Context-Similarity Collaborative Filtering Recommendation
This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In
Hiep Xuan Huynh +6 more
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