Towards data variation trends recommendation
Authors: 
Tuan Thanh Nguyen, Phuc Quang Tran, Hoang Tan Nguyen, Toan Phung Huynh, Hanh My Thi Le, Hiep Xuan HuynhAuthors Info & Claims
Pages 117 - 122
Abstract
Present study on recommender systems mainly focuses on the logical nature of the existence or non-existence of a priority relationship between the user and data item, regardless of the ratio or implicative relationship based on statistics between users and data items in a particular context. Therefore, this report proposes a new approach to recommender systems based on data variation trends; such method will help form a new approach to recommender systems on basis of knowledge available in the form of implicity by computation of partial derivatives for interestingness measurements. In addition, experiments aim at evaluating the effectiveness of the proposed model with traditional models based on using MSWeb dataset as empirical data, comparing and discussing the results obtained from the proposed model.
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Phan NQ, Huynh HX, Guillet F, Gras R. Classifying objective interestingness measures based on the tendency of value variation. 8th International Meeting on Statistical Implicative Analysis 2015; 4: 143--172.
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Index Terms
- Towards data variation trends recommendation
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Published In
January 2019
268 pages
ISBN:9781450366120
DOI:10.1145/3310986
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Association for Computing Machinery
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Published: 25 January 2019
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ICMLSC 2019
ICMLSC 2019: 2019 the 3rd International Conference on Machine Learning and Soft Computing
January 25 - 28, 2019
Da Lat, Viet Nam
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