Attribute and Global Boosting: A Rating Prediction Method in Context-Aware Recommendation

Zheng, Lin; Zhu, Fang; Alshahrani, Mohammed

doi:10.1093/comjnl/bxw016

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…In addition to those single models, ensemble learning [28]- [31] can be useful in CAR. For example, [29] employed gradient boosting techniques to organize and optimize rich attributes in a context-aware factorization framework. Reference [28] also utilized boosting trees to mine and integrate the global and local preferences of users.…”

Section: A New User Problemmentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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Predicting new user behavior has always been a challenging issue in intelligent recommender systems. This challenge is mainly due to the extreme asymmetry of information between new users and old users. Existing factorization models can efficiently process and map asymmetric information, but they are not good at mining deep relationships between contexts when compressing high-dimensional data. In contrast, neural network methods can deeply exploit the relationship between contexts; however, their training cost is much larger than factorization approaches. Therefore, this paper proposes a scalable and efficient recommender to solve the new user problem by filling the gap between factorization models and neural networks. The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. The efficient part is a factorization model based on a correlation constraint mechanism and a projection strategy, which enables an asymmetric mapping of information between old and new users. The entire recommender fuses the two parts so that the factorization model and the neural network can complement each other. The experimental results show that our approach can achieve a good balance between performance and training efficiency compared to state-of-the-art methods. INDEX TERMS Recommender systems, new user problems, user behavior, context-aware recommendation, efficient training.

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Section: A New User Problemmentioning

confidence: 99%

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

et al. 2020

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“…CTM [1] is an important recommendation strategy that mainly uses MF with topic modeling to complete a recommendation task. MF [15]- [17] is a representative approach in CF to obtain accurate recommendations, while topic modeling can assist recommendation by extracting interesting topics from relevant contents [18]. One typical approach is the Hidden Factors and Hidden Topics (HFT) model [2], which bridges the gap between the latent vectors in MF models and the probabilistic vectors obtained from topic models.…”

Section: Related Work a Collaborative Topic Modelingmentioning

confidence: 99%

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

et al. 2020

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Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.

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“…Context-Aware Recommender Systems (CARS) [1] has been introduced as a way to solve the data sparsity and cold start problems in the recommender systems, and proved to be providing recommendation lists with high accuracy and user satisfaction [2], [3]. Among various types of auxiliary information, item description can express the user's view directly which can't be translated by computer.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation

et al. 2019

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The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors.

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Attribute and Global Boosting: A Rating Prediction Method in Context-Aware Recommendation

Cited by 8 publications

References 23 publications

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Factorization Meets Neural Networks: A Scalable and Efficient Recommender for Solving the New User Problem

Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction

Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation

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