Factorization Machines (FMs) have been proven effective in modeling the second-order interactions between attributes. Nevertheless, existing methods often fall short of learning the complex and rich intra-attribute feature interactions, which are abundantly exhibited in real-world recommendation data. To address this problem, we propose an FM framework equipped with generalized metric learning to better handle the essential feature correlations. In particular, based on this framework, we present a Mahalanobis distance and a deep neural network method, 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 significant margin.

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.

Modeling user behaviors in an interactive information seeking process is key to understand user information need. Two prime characteristics of interactive information seeking are statefulness and participant initiatives. There are many work focus on click modeling using a search session log. However, such a search session of user queries and clicks lacks initiative information between user and system to model user state. In this research proposal, we focus on product search in E-commerce. We augment the search session with generalized initiative information. We integrate long term user features into search sessions and formulate user modeling as a Markov Decision Process (MDP). We further investigate how proposed user modeling would benefit Learning to Rank (LTR) in interactive stateful information seeking process.