Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
|Place of Conferral||中国科学院自动化研究所|
|Keyword||社会媒体 序列行为建模 用户建模 序列推荐 多模态 异构信息网络 知识图谱 可解释性|
The rapid development of the Internet allows the masses to enter the online world. Social media is flourishing on the fertile ground of the Internet. Social media applications have penetrated into people's lives, affecting people's daily lives. Users are the producers of social media content and the ultimate goal of their services. Various applications on the Internet provide users with convenient services while also recording user behavior data. User behavior is a potential expression of user interest preferences. Modeling user behaviors can capture user's interest preferences in social media and support different downstream applications, such as personalized recommendation systems and personalized search. User behaviors on social media change dynamically over time. Modeling on users' sequential behaviors in social media is an important means to effectively follow the evolution of users' dynamic interests and capture their long-term and short-term interest preferences. In addition, there are still several key issues in the modeling of user sequential behaviors, such as the multi-modality of the content generated by users on social media, the heterogeneity and polysemy property of user behaviors, the complex interaction between users and items, and the typical applications of models lack Interpretability. In this thesis, we aim to tackle four key issues of user sequential behavior modeling on social media, to improve the efficiency and accuracy of user interest capturing, and provide strong support for downstream applications. The major contributions of this thesis are as follows:
1. Research on multi-modal content modeling on social media. The basis of sequential behavior modeling is to model the multi-modal content of user behavior. Social media is full of multi-modal content, such as text, images, video, audio, and more. When designing various applications that serve users, multi-modal content is raw materials. Analysis of multi-modal content and the extraction of useful information for the task are beneficial to mining the underlying patterns to improve the performance of the applications. Therefore, we study two aspects of multi-modal content modeling, laying the foundation for follow-up research. The first one is to model user behaviors based on multi-modal content. We propose a method to automatically predict the popularity of pictures posted by users on Flickr by the stacking model and the specially designed explicit and implicit features. The second one is to model social relationships based on multi-modal content. We propose an effective social relationship labeling model with the specially designed multi-modal similarity features constructed from metadata and the relationship of users' Twitter list, to assist in the construction of social networks.
2. Contextual sequential behavior modeling based on user's heterogeneous and polysemy behaviors. A user's sequential behaviors can reveal the evolution of user interests. The sequential recommendation is an important task for user-oriented online services. There are many characteristics of user behaviors on social networks: user behaviors are inherently heterogeneous, user behaviors have the polysemy property, and user behaviors are dynamically context-dependent. we propose a unified contextual self-attention network to address the three properties. Heterogeneous user behaviors considered in our model are projected into a common latent semantic space. Then the output is fed into the feature-wise self-attention network to capture the polysemy of user behaviors. Besides, the forward and backward position encoding matrices are proposed to model dynamic contextual dependency. The proposed method can effectively capture the dynamic interest expression of user behavior, and can greatly improve the computing efficiency of the model.
3. Interactive heterogeneous information network enhanced comprehensive sequential behavior modeling. The existing sequential recommendation algorithms usually use historical sequential behaviors to model user preferences, which only take into account local information about user behaviors, so that it has limited expressive power in capturing complex user preferences. We propose a meta-path augmented context-aware sequence recommendation method, which introduces heterogeneous information networks and meta-path mechanism into the recommendation system, and builds global context that contains the potential dependencies between users and items, which is helpful for users to expand their interest through the dense connections in the network in a reasonable manner. The proposed approach introduces meta-paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods which only consider the local preference, our proposed method takes the advantages of incorporating global context information which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt the co-attention mechanism to model complex interactions between the global context and users’ historical behaviors for better user representations.
4. Explainable sequential behavior modeling based on knowledge graph (KG) reasoning. The current neural network-based methods have achieved high accuracy in recommendation tasks. However, most methods have not considered providing a reasonable explanation for users while making recommendation decisions. We propose a novel explainable interaction-driven user modeling algorithm to exploit the knowledge graph for constructing an effective and explainable sequential recommender. Qualified semantic paths between specific user-item pair are extracted from KG. Encoding those semantic paths and learning the importance scores for each path provides the path-wise explanation for the recommendation system. Different from traditional item-level sequential modeling methods, we capture the interaction-level user dynamic preferences by modeling the sequential interactions. It is a high-level representation that contains auxiliary semantic information from KG. Furthermore, we adopt a joint learning manner for better representation learning by employing multi-modal fusion, which benefits from the structural constraints in KG and involves three kinds of modalities. The proposed algorithm achieves impressive performance in terms of endowing sequential recommendation systems with accuracy and Explainability.
|黄晓雯. 社会媒体上下文感知的序列行为建模[D]. 中国科学院自动化研究所. 中国科学院大学,2020.|
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