Publication statistics

Pub. period:2009-2011
Pub. count:4
Number of co-authors:1


Number of publications with 3 favourite co-authors:

Peter Brusilovsky:



Productive colleagues

Danielle H. Lee's 3 most productive colleagues in number of publications:

Peter Brusilovsky:63

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Danielle H. Lee


Publications by Danielle H. Lee (bibliography)

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Lee, Danielle H. and Brusilovsky, Peter (2011): Improving recommendations using WatchingNetworks in a social tagging system. In: Proceedings of the 2011 iConference 2011. pp. 33-39.

This paper aims to examine whether users' watching networks can improve collaborative filtering-based recommendations (CF). Watching networks are established by users upon their perceived usefulness or interests about other users' information collections. The networks do not require mutual agreement between a watching party and a watched party. The typical example of this network is 'following' in Twitter, 'watching' on CiteULike, or 'contacts' on Flickr. Once a user declares that 'I want to watch user A', the user A's information collection is displayed to the watching user, continuously. It can be interpreted to mean that a watching user found some shared interests in user A's collection and want to refer to it in future. The approaches explored in this paper take advantage of this watching network as a part of user's preferences for recommendations. To evaluate the potential of these approaches, we focus on a social tagging system, CiteULike. Our data shows that in this context, a hybrid recommendation approach that fuses CF and watching network-based recommendations outperforms both CF and network-based recommendations.

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Lee, Danielle H. and Brusilovsky, Peter (2010): Social networks and interest similarity: the case of CiteULike. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia 2010. pp. 151-156.

In collaborative filtering recommender systems, there is little room for users to get involved in the choice of their peer group. It leaves users defenseless against various spamming or ''shilling'' attacks. Other social Web-based systems, however, allow users to self-select peers and build a social network. We argue that users' self-defined social networks could be valuable to increase the quality of recommendation in CF systems. To prove the feasibility of this idea we examined how similar are interests of users connected by self-defined relationships in a collaborative tagging systems Citeulike. Interest similarity was measured by similarity of items and meta-data they share and tags they use. Our study shows that users connected by social networks exhibit significantly higher similarity on all explored levels (items, meta-data, and tags) than non-connected users. This similarity is the highest for directly connected users and decreases with the increase of distance between users. Among other interesting properties of information sharing is the finding that between-user similarity in social connections on the level of metadata and tags is much larger than similarity on the level of items. Overall, our findings support the feasibility of social network based recommender systems and offer some good hints to the prospective authors of these systems.

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Lee, Danielle H. and Brusilovsky, Peter (2010): Using self-defined group activities for improving recommendations in collaborative tagging systems. In: Proceedings of the 2010 ACM Conference on Recommender Systems 2010. pp. 221-224.

This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.

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Lee, Danielle H. and Brusilovsky, Peter (2009): Reinforcing Recommendation Using Implicit Negative Feedback. In: Proceedings of the 2009 Conference on User Modeling, Adaptation and Personalization 2009. pp. 422-427.

Recommender systems have explored a range of implicit feedback approaches to capture users' current interests and preferences without intervention of users' work. However, current research focuses mostly on implicit positive feedback. Implicit negative feedback is still a challenge because users mainly target information they want. There have been few studies assessing the value of negative implicit feedback. In this paper, we explore a specific approach to employ implicit negative feedback and assess whether it can be used to improve recommendation quality.

© All rights reserved Lee and Brusilovsky and/or their publisher

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