Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Hybrid web recommender systems
The adaptive web
Collaborative location and activity recommendations with GPS history data
Proceedings of the 19th international conference on World wide web
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
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In this paper, we propose a new model to integrate additional data, which is obtained from geospatial resources other than original data set in order to improve Location/Activity recommendations. The data set that is used in this work is a GPS trajectory of some users, which is gathered over 2 years. In order to have more accurate predictions and recommendations, we present a model that injects additional information to the main data set and we aim to apply a mathematical method on the merged data. On the merged data set, singular value decomposition technique is applied to extract latent relations. Several tests have been conducted, and the results of our proposed method are compared with a similar work for the same data set.