Personalized recommendation in social tagging systems using hierarchical clustering

By Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke. ACM Conference on Recommender Systems, 2008

597 citations recorded by the source corpus.

Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successful in both cases, our results suggest that folksonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies.

Subfields: Information Systems, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2537f07d31dac9b884b3d9c26e48ffd917d8e5a5

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Citations in the corpus

Collaborative Filtering beyond the User-Item Matrix (2014, background): “Another category of approaches focused on tags as side information [Firan et al. 2007; Shepitsen et al. 2008].”
Latent dirichlet allocation for tag recommendation (2009, background): “In [27], Shepitsen et al. propose a resource recommendation system based on hierarchical clustering of the tag space.”
SemRec: A Semantic Enhancement Framework for Tag Based Recommendation (2011, methodology): “Then we employ the Hierarchical Agglomerative Clustering algorithm on this matrix for clustering tags (Shepitsen et al. 2008).”
Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “Following existing works [12, 31], we employ the widely used leave-one-out evaluation protocol in the evaluation stage.”
Recommender Systems (2012, methodology): “To alleviate these problems, advanced methods such as tag hierarchical clustering [269], introduction of ontologies [270] and recommendation of tags [271] were proposed.”
Recommender Systems (2016, unknown): “A hierarchical clustering method for content-based recommendation was proposed in [542].”
Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling (2016, methodology): “Consequently, many eﬀorts have been put on tag-aware personalized recommendation using content-based ﬁltering [2, 8] or collaborative ﬁltering [1, 9].”
Recommender systems survey (2013, background): “RS comprising social information have been clustered to improve the following areas: tagging [208], explicit social links [179] and explicit trust information [181,70].”