ﻻ يوجد ملخص باللغة العربية
Characterizing users interests accurately plays a significant role in an effective recommender system. The sequential recommender system can learn powerful hidden representations of users from successive user-item interactions and dynamic users preferences. To analyze such sequential data, conventional methods mainly include Markov Chains (MCs) and Recurrent Neural Networks (RNNs). Recently, the use of self-attention mechanisms and bi-directional architectures have gained much attention. However, there still exists a major limitation in previous works that they only model the users main purposes in the behavioral sequences separately and locally, and they lack the global representation of the users whole sequential behavior. To address this limitation, we propose a novel bidirectional sequential recommendation algorithm that integrates the users local purposes with the global preference by additive supervision of the matching task. We combine the mask task with the matching task in the training process of the bidirectional encoder. A new sample production method is also introduced to alleviate the effect of mask noise. Our proposed model can not only learn bidirectional semantics from users behavioral sequences but also explicitly produces user representations to capture users global preference. Extensive empirical studies demonstrate our approach considerably outperforms various state-of-the-art models.
Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters. The typical method to utilize the glyph features is by incorporating them into
Knowledge graph (KG), as the side information, is widely utilized to learn the semantic representations of item/user for recommendation system. The traditional recommendation algorithms usually just depend on user-item interactions, but ignore the in
Disentangled representation has been widely explored in many fields due to its maximal compactness, interpretability and versatility. Recommendation system also needs disentanglement to make representation more explainable and general for downstream
Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a data
This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up app