Graph-based context-aware collaborative filtering
| Autor: | Do Thi Lien, Nguyen Duy Phuong, Tu Minh Phuong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Transitive relation Computer science business.industry General Engineering 02 engineering and technology Recommender system Similarity measure Machine learning computer.software_genre Graph Computer Science Applications k-nearest neighbors algorithm Data set 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Collaborative filtering Graph (abstract data type) 020201 artificial intelligence & image processing Artificial intelligence business computer Sparse matrix |
| Zdroj: | Expert Systems with Applications. 126:9-19 |
| ISSN: | 0957-4174 |
| Popis: | Context-aware recommender systems (CARS) are specially designed to take into account the contextual conditions under which a user experiences an item, with the goal of generating improved recommendations. A known difficulty when constructing recommender systems is data sparseness, which reduces the effectiveness of collaborative filtering algorithms. While using contextual information provides fine-grained signals for the recommendation process, it makes the data even sparser and increases the computational complexity. In this paper, we present a method for making context-aware recommendations, which is less sensitive to data sparseness. The proposed method exploits the transitivity of the interactions between users and items on the user-item graph to augment the direct interactions, thus reducing the negative effect of sparse data. Based on graph transitivity we introduce a new graph-based association measure that we use as a measure of similarity between two users or two items in nearest neighbor recommendation methods. This combination of graph-based similarity measure with nearest neighbor methods allows considering more contextual conditions at a lower risk of being affected by data sparseness caused by additional contextual dimensions. We experimentally evaluated the proposed method on three contextually-tagged data sets. The results show that our method outperforms several baselines and state-of-the-art context-aware recommendation methods. |
| Databáze: | OpenAIRE |
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