Personalized PageRank vectors for tag recommendations

Kim, Heung-Nam; Saddik, Abdulmotaleb El

doi:10.1145/2043932.2043945

Cited by 26 publications

(14 citation statements)

References 16 publications

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“…Methodologies relying on the folksonomy data include Hypergraph [12][10], Graph [5][9] [6] and Collaborative [2][7] [13] approaches. While hypergraph approaches try to capture and analyse all characteristics of the data in their models, graph-based and collaborative approaches can be described as reductionist methods since they reduce the 3-dimensional folksonomy data to one or several 2-dimensional projections.…”

Section: Related Workmentioning

confidence: 99%

“…The final scores are calculated by subtracting from the weight of each tag in the personalised ranking for ui and dj, the weight of the tag in the general ranking. Although this produces better results than using the personalised ranking alone, it was shown in [6] that a simpler strategy of setting the preference weight of all nodes not included in the test post to 0 instead of 1 gives an equal ranking to the differential approach. After running experiments to confirm that the two strategies indeed give equal results, we adopt the approach of [6] due to its faster runtime.…”

Section: Folkrankmentioning

confidence: 99%

See 1 more Smart Citation

Extending FolkRank with content data

Landia

Anand

Hotho

et al. 2012

Proceedings of the 4th ACM RecSys Workshop on Recommender Systems and the Social Web

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Real-world tagging datasets have a large proportion of new/ untagged documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for new documents, approaches are required which model documents not only based on the tags assigned to them in the past (if any), but also the content.In this paper we present a novel adaptation to the widely recognised FolkRank tag recommendation algorithm by including content data. We adapt the FolkRank graph to use word nodes instead of document nodes, enabling it to recommend tags for new documents based on their textual content. Our adaptations make FolkRank applicable to post-core 1 ie. the full real-world tagging datasets and address the new item problem in tag recommendation. For comparison, we also apply and evaluate the same methodology of including content on a simpler tag recommendation algorithm. This results in a less expensive recommender which suggests a combination of user related and document content related tags.Including content data into FolkRank shows an improvement over plain FolkRank on full tagging datasets. However, we also observe that our simpler content-aware tag recommender outperforms FolkRank with content data. Our results suggest that an optimisation of the weighting method of FolkRank is required to achieve better results.

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Section: Related Workmentioning

confidence: 99%

Section: Folkrankmentioning

confidence: 99%

Extending FolkRank with content data

Landia

Anand

Hotho

et al. 2012

Proceedings of the 4th ACM RecSys Workshop on Recommender Systems and the Social Web

View full text Add to dashboard Cite

show abstract

“…One key advantage of this teleportation vector modification based approach over modifying the transition matrix, as in [12], is that the term β (in Equation 2) can be used to directly control the degree of seeding (or personalization) of the PPR score. In fact, these personalized random-walk and PageRank based measures of node significance have been shown to be highly effective in many prediction and recommendation applications [1,29], where it is necessary to rank the nodes of the graph relative to a given set of (seed) nodes that represent the user's interest. Therefore, in this paper, we rely on this PPR based formulation of personalized, context-sensitive node significance.…”

Section: Context-sensitive Node Significance and Personalizationmentioning

confidence: 99%

Reducing seed noise in personalized PageRank

Huang

Candan

et al. 2016

Soc. Netw. Anal. Min.

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Network based recommendation systems leverage the topology of the underlying graph and the current user context to rank objects in the database. Random-walk based techniques, such as PageRank, encode the structure of the graph in the form of a transition matrix of a stochastic process from which the significances of the nodes in the graph are inferred. Personalized PageRank (PPR) techniques complement this with a seed node set which serves as the personalization context. In this paper, we note (and experimentally show) that PPR algorithms that do not differentiate among the seed nodes may not properly rank nodes in situations where the seed set is incomplete and/or noisy. To tackle this problem, we propose alternative robust personalized PageRank (RPR) strategies, which are insensitive to noise in the set of seed nodes and in which the rankings are not overly biased towards the seed nodes. In particular, we show that novel teleportation discounting and seed-set maximal PPR techniques help eliminate harmful bias of individual seed nodes and provide effective seed differentiation to lead to more accurate rankings. We also show that the proposed techniques lead to efficient implementations, where existing approximation algorithms and/or parallel implementations for computing the PPR scores can be easily leveraged. Moreover, the proposed formulations are reuse-promoting in the sense that, it is possible to divide the work relative to individual seed nodes and cache the intermediary results obtained during the computation, and especially in systems with large query throughputs, it may be possible to cluster queries based on the partial overlaps between the seed sets and reduce the overall robust PPR computation costs. Experiment results show that the proposed techniques are efficient and highly effective in improving recommendations and eliminating unwanted bias due to imperfections in the seed set.

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“…Methodologies relying on the folksonomy data include Hypergraph [12][10], Graph [4][9] [5] and Collaborative [1][7] [13] approaches. While hypergraph approaches try to capture and analyse all characteristics of the data in their models, graph-based and collaborative approaches can be described as reductionist methods since they reduce the 3-dimensional folksonomy data to one or several 2-dimensional projections.…”

Section: Related Workmentioning

confidence: 99%

Utilising document content for tag recommendation in folksonomies

Landia

2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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Real-world tagging datasets have a large proportion of new/ unseen documents. Few approaches for recommending tags to a user for a document address this new item problem, concentrating instead on artificially created post-core datasets where it is guaranteed that the user as well as the document of each test post is known to the system and already has some tags assigned to it. In order to recommend tags for unseen documents, approaches are required which model documents not only based on the tags assigned to it in the past (if any), but also the content.The focus of my research is on utilising the content of documents in order to address the new item problem in tag recommendation. I apply this methodology first to simple baseline tag recommenders and then the more advanced tag recommendation algorithm FolkRank [3][4].One of my main contributions is a novel adaptation to the FolkRank graph model to use multiple word nodes instead of a single document node to represent each document. This enables FolkRank to recommend tags for unseen documents and makes it applicable to full real-world tagging datasets, addressing the new item problem in tag recommendation.

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Personalized PageRank vectors for tag recommendations

Cited by 26 publications

References 16 publications

Extending FolkRank with content data

Extending FolkRank with content data

Reducing seed noise in personalized PageRank

Utilising document content for tag recommendation in folksonomies

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