基于用户画像的图书馆推荐服务初探
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摘要
面对图书馆信息过载的现状,如何构建用户画像,基于读者入馆行为数据为读者提供个性化的推荐服务,是图书馆实践大数据应用需要解决的问题。本文试图在图书馆广泛的读者行为框架中定位用户与应用系统的互动需求,探讨可以更好地在读者与INNOPAC等应用系统的互动关系中发挥良好作用的推荐方式。
Facing information overload in library community, it is an urgent problem for libraries practicing big data application to explore strategies of building users' profile and provide personalized recommendation services based on reader behavior. This paper attempts to locate the interactive demand between readers and application systems in the library reader behavior framework. How and what role that recommendations should play in a user's relationship with an INNOPAC.
Facing information overload in library community, it is an urgent problem for libraries practicing big data application to explore strategies of building users' profile and provide personalized recommendation services based on reader behavior. This paper attempts to locate the interactive demand between readers and application systems in the library reader behavior framework. How and what role that recommendations should play in a user's relationship with an INNOPAC.
引文
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7 . Dave Pattern’s Blog. More stuff like this[EB/OL].[2018-09-03].http://library.hud.ac.uk/australia/perl/test/rec2.pl.
8 . Dave Pattern.Sliding down the long tail[EB/OL].[2018-09-03].https://www.daveyp.com/2011/03/23/sliding-down-the-long-tail/.
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10 . Yager R R. Fuzzy logic methods in recommender systems[J].Fuzzy Sets and Systems,2003, 136(2):133-149.
11 . Chen L, Pu P. Preference-based organization interfaces:aiding user critiques in recommender systems[J]. Lecture Notes in Computer Science, 2007, 45(11):77-86.
12 . Lu R. Knowledge engineering and knowledge science at the turn of the century[M].Beijing:Tsinghua University Press, 2001.
13 . Dunning T. Accurate methods for the statistics of surprise and coincidence[M]. Boston:MIT Press, 1993.
2 . Prekopcsák Z. Content organization and Discovery:State-ofthe-art and New Ideas for P2P-Fusion[EB/OL].[2018-09-03]. http://prekopcsak.hu/papers/preko-2007-p2pfusion.pdf.
3 . LibraryThing[EB/OL].[2018-09-03].http://librarything.com/.
4 . Monnich M, Spierling M. Adding value to the library catalog by implementing a recommendation system[J/OL].[2018-09-03]. www.dlib.org/dlib/may08/monnich/05monnich.html.
5 . Baez, M, Birukou A, Casati F, et al. Addressing information overload in the scientific community[J]. Internet Computing, 2010, 14(6):31-38.
6 . Dave Pattern’s Blog. People who looked at this thing, also looked at this stuff[EB/OL].[2012-08-03]. http://www.daveyp.com/2012/02/19/People-who-looked-at-this-thing-also-looked-at-this-stuff/.
7 . Dave Pattern’s Blog. More stuff like this[EB/OL].[2018-09-03].http://library.hud.ac.uk/australia/perl/test/rec2.pl.
8 . Dave Pattern.Sliding down the long tail[EB/OL].[2018-09-03].https://www.daveyp.com/2011/03/23/sliding-down-the-long-tail/.
9 . Cherry J M, Clinton M. A Profile of User Background and User Satisfaction With the University of Toronto OPAC and the Implications for User Training and User Interfaces[C].Proceedings of the 52nd Annual Meeting of the American Society for Information Science,Washington, D. C., 1989:121-128.
10 . Yager R R. Fuzzy logic methods in recommender systems[J].Fuzzy Sets and Systems,2003, 136(2):133-149.
11 . Chen L, Pu P. Preference-based organization interfaces:aiding user critiques in recommender systems[J]. Lecture Notes in Computer Science, 2007, 45(11):77-86.
12 . Lu R. Knowledge engineering and knowledge science at the turn of the century[M].Beijing:Tsinghua University Press, 2001.
13 . Dunning T. Accurate methods for the statistics of surprise and coincidence[M]. Boston:MIT Press, 1993.