Analyzing User Behavior to Rank Desktop Items

Proceedings of the 18th ACM Conference on Information and Knowledge Management

2009

Many users need to refer to content in existing files (pictures, tables, emails, web pages and etc.) when they write documents (programs, presentations, proposals and etc.), and often need to revisit these referenced files for review, revision or reconfirmation. Therefore it is meaningful to discover an approach to help users revisit these references effectively. Traditional approaches(file explorer, desktop search, and etc.) fail to work in this case. In this paper, we propose an efficient solution for this problem. We firstly define a new personal data relationship: Context-based Reference(CR), which is generated by user behaviors. We also propose efficient methods to identify CR relationship and present a new type of query based on it: Context-based Query(CQuery), which helps users efficiently revisit personal documents based on CR relationship. Our experiments validate the effectiveness and efficiency of our methods.

“…Studies [10] show that the two items accessed continuously often has associations. Inspired from this idea, we propose a method for identifying CR relationship based on user behaviors.…”

Section: Pds Enginementioning

confidence: 99%

Supporting context-based query in personal DataSpace

Proceedings of the 18th ACM Conference on Information and Knowledge Management

2009

“…Because most desktop search tools do not distinct known items from desktop items when creating index, their performance are often very poor. Reference [8] proposes to rank personal data items by exploiting user behaviors. Reference [9] focuses on improving recall by specific ranking policies.…”

Section: A Related Workmentioning

confidence: 99%

An Efficient Method for Constructing Personal DataSpace

2009 Sixth Web Information Systems and Applications Conference

Kou

2009

Abstract-With increment of personal data amount, how to efficiently manage Personal DataSpace(PDS) becomes a serious problem and a hot research topic. In PDS, users often expect to relocate some known items. Therefore how to efficiently identify these known items from a great number of objects becomes an important research issue. To the best of our knowledge, there is no existing works tackling this problem. In this paper, we propose a concept KnownBy to model the basic relationship between data items and users, and take it as the baseline for deciding if an item belongs to PDS. By analyzing user access logs, we mine some rules for identifying known items, and propose efficient methods for marking known items from personal desktop resources. Our experiments validate the effectiveness and efficiency of our methods, which can be integrated into personal information management systems, such as desktop search tools, and etc.

“…Desktop search engine (DSE), e.g. Google desktop search [15], is another attempt for PIM, and there are some approaches [16] presented by researchers to improve the efficiency of DSE. But DSE does not allow structural information to be exploited for queries [2].…”

Section: Query and Indexmentioning

confidence: 99%

Research on personal dataspace management

Proceedings of the 2nd SIGMOD PhD Workshop on Innovative Database Research

2008

Explosion of the amount of digital information has made Personal Information Management(PIM) become a hot topic. Personal data is always distributed, rough-and-tumble, personalized, heterogenous and evolutionary, which brings much challenge to to effective and efficient Personal Dataspace Management (PDSM). In the paper, by highlighting the importance of users in Personal Dataspace Management System(PDSMS), we proposed a user-centered framework. We first show research issues, related work, main research problems and challenges in this area. We then introduce the current research work and the preliminary results. Finally, the research plan of my PhD project is presented for discussion.