INCREMENTAL USER MODELING WITH HETEROGENEOUS USER BEHAVIORS

Keyword(s):  
1987 ◽  
Vol 31 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Matthew P. Anderson ◽  
James E. McDonald ◽  
Roger W. Schvaneveldt

Models of users' procedural knowledge were derived from the records of command usage obtained from nine experienced users of the Unix operating system. Pairwise transitions between user command entries were analyzed for the purpose of identifying salient command patterns associated with task-based user behaviors. Structural models of command usage patterns were obtained from Pathfinder network scaling of Unix command events. The network representation of command patterns was evaluated as a method for abstracting users' procedural knowledge. These network scaling solutions revealed patterns that were common both within and across users' command usage.


Author(s):  
Chuhan Wu ◽  
Fangzhao Wu ◽  
Yongfeng Huang ◽  
Xing Xie

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.


2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ko-Hsun Huang ◽  
Yi-Shin Deng ◽  
Ming-Chuen Chuang

User modeling and profiling has been used to evaluate systems and predict user behaviors for a considerable time. Models and profiles are generally constructed based on studies of users’ behavior patterns, cognitive characteristics, or demographic data and provide an efficient way to present users’ preferences and interests. However, such modeling focuses on users’ interactions with a system and cannot support complicated social interaction, which is the emerging focus of serious games, educational hypermedia systems, experience, and service design. On the other hand, personas are used to portray and represent different groups and types of users and help designers propose suitable solutions in iterative design processes. However, clear guidelines and research approaches for developing useful personas for large-scale and complex social networks have not been well established. In this research, we reflect on three different design studies related to social interaction, experience, and cross-platform service design to discuss multiple ways of identifying both direct users and invisible users in design research. In addition, research methods and attributes to portray users are discussed.


Author(s):  
Chuhan Wu ◽  
Fangzhao Wu ◽  
Tao Qi ◽  
Yongfeng Huang

Modeling user interest is critical for accurate news recommendation. Existing news recommendation methods usually infer user interest from click behaviors on news. However, users may click a news article because attracted by its title shown on the news website homepage, but may not be satisfied with its content after reading. In many cases users close the news page quickly after click. In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation. More specifically, we propose a personalized reading speed metric to measure users’ satisfaction with news content. We learn embeddings of users from the news content they have read and their satisfaction with these news to model their interest in news content. In addition, we also learn another user embedding from the news titles they have clicked to model their preference in news titles. We combine both kinds of user embeddings into a unified user representation for news recommendation. We train the user representation model using two supervised learning tasks built from user behaviors, i.e., news title based click prediction and news content based satisfaction prediction, to encourage our model to recommend the news articles which not only are likely to be clicked but also have the content satisfied by the user. Experiments on real-world dataset show our method can effectively boost the performance of user modeling for news recommendation.


Author(s):  
Bamshad Mobasher ◽  
Styliani Kleanthous ◽  
Michael Ekstrand ◽  
Bettina Berendt ◽  
Jahna Otterbacher ◽  
...  
Keyword(s):  

2021 ◽  
Vol 285 ◽  
pp. 116429
Author(s):  
Wen-Long Shang ◽  
Jinyu Chen ◽  
Huibo Bi ◽  
Yi Sui ◽  
Yanyan Chen ◽  
...  

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