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A personalized search Model Using Online Social Network Data Based on a Holonic Multiagent System

China Communications 2020年第2期17卷 176-205页

作者： Meijia Wang Qingshan Li Yishuai Lin School of Computer Science and Technology Xidian University

personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through ***,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized *** paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search *** proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the *** experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

关键词： personalized search online social network holonic multiagent system

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Community-Aware Resource Profiling for personalized search in Folksonomy

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Journal of Computer Science & Technology 2012年第3期27卷 599-610页

作者：谢浩然李青蔡毅 Department of Computer Science City University of Hong Kong School of Software Engineering South China University of Technology

In recent years, there is a fast proliferation of collaborative tagging （a.k.a. folksonomy） systems in Web 2.0 communities. With the increasingly large amount of data, how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem. Collaborative tagging systems provide an environment for users to annotate resources, and most users give annotations according to their perspectives or feelings. However, users may have different perspectives or feelings on resources, e.g., some of them may share similar perspectives yet have a conflict with others. Thus, modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable. We propose, to tackle this problem in this paper, a community-aware approach to constructing resource profiles via social filtering. In order to discover user communities, three different strategies are devised and discussed. Moreover, we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function, to optimize personalized resources ranking based on user preferences and user issued query. We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods. The experimental results verify our observations and effectiveness of proposed method.

关键词： tagging personalized search user community social filtering

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AOL4PS:A Large-scale Data Set for personalized search

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Data Intelligence 2021年第4期3卷 548-567页

作者： Qian Guo Wei Chen Huaiyu Wan School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Beijing Key Laboratory of Traflc Data Analysis and Mining Beijing 100044China

personalized search is a promising way to improve the quality of Websearch,and it has attracted much attention from both academic and industrial *** of the current related research is based on commercial search engine data,which can not be released publicly for such reasons as privacy protection and information *** leads to a serious lack of accessible public data sets in this *** few publicly available data sets have not become widely used in academia because of the complexity of the processing process required to study personalized search *** lack of data sets together with the difficulties of data processing has brought obstacles to fair comparison and evaluation of personalized search *** this paper,we constructed a large-scale data set AOL4 PS to evaluate personalized search methods,collected and processed from AOL query *** present the complete and detailed data processing and construction ***,to address the challenges of processing time and storage space demands brought by massive data volumes,we optimized the process of data set construction and proposed an improved BM25 *** are performed on AOL4 PS with some classic and state-of-the-art personalized search methods,and the experiment results demonstrate that AOL4 PS can measure the effect of personalized search models.

关键词： personalized search Text data processing Data set construction

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Ranking of Web Pages in a personalized search

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Journal of Computer and Communications 2023年第2期11卷 89-101页

作者： Mahmoud Abou Ghaly Mathematics Department Faculty of Science Ain Shams University Cairo Egypt Information Technology Department Faculty of Computer Science and Information Technology Al-Baha University Al-Baha KSA

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

关键词： Implicit Feedback personalized search Web Page Ranking User Profile

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基于SAM模型的用户兴趣表示研究

基于SAM模型的用户兴趣表示研究

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第二十三届中国数据库学术会议

作者：田萱杜小勇中国人民大学信息学院

1前言除了用户兴趣,用户上下文中的认知结构也可以帮助理解用户个性化需求。受认知心理学上"激活-扩散模型"的启发,我们曾提出一种基于认知结

关键词： Cognitive psychology Spreading-activation model(SAM) User interest personalized search

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