Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 34, Issue 8, Pages 3740-3753Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2020.3034613
Keywords
Frequency modulation; Correlation; Euclidean distance; Motion pictures; Recommender systems; Neural networks; Recommender system; factorization machine; metric learning
Categories
Funding
- National Natural Science Foundation of China [61902223, U1936203]
- Innovation Teams in Colleges and Universities in Jinan [:2018GXRC014]
- Shandong Provincial Natural Science and Foundation [ZR2019JQ23]
- Young Creative Team in Universities of Shandong Province [:2020KJN012]
- New AI Project towards the Integration of Education and Industry in QLUT
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This paper proposes a Factorization Machines (FM) framework equipped with generalized metric learning techniques to better capture feature correlations. Specifically, a Mahalanobis distance and a deep neural network (DNN) method are introduced to effectively model linear and non-linear correlations, respectively. Experimental results demonstrate that our framework outperforms several state-of-the-art baselines on benchmark datasets.
Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors. The problem is that the inner product violates the triangle inequality property of feature vectors. As a result, it cannot well capture fine-grained attribute interactions, resulting in sub-optimal performance. Recently, the euclidean distance is exploited in FMs to replace the inner product and has delivered better performance. However, previous FM methods including the ones equipped with the euclidean distance all focus on the attribute-level interaction modeling, ignoring the critical intrinsic feature correlations inside attributes. Thereby, they fail to model the complex and rich interactions exhibited in the real-world data. To tackle this problem, in this paper, we propose a FM framework equipped with generalized metric learning techniques to better capture these feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network (DNN) methods, which can effectively model the linear and non-linear correlations between features, respectively. Besides, we design an efficient approach for simplifying the model functions. Experiments on several benchmark datasets demonstrate that our proposed framework outperforms several state-of-the-art baselines by a large margin. Moreover, we collect a new large-scale dataset on second-hand trading to justify the effectiveness of our method over cold-start and data sparsity problems in recommender systems.
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