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. ER - TY - CONF AU - Landia, Nikolas AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Jäschke, Robert AU - Doerfel, Stephan AU - Mitzlaff, Folke A2 - T1 - Extending FolkRank with content data T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM CY - New York, NY, USA PY - 2012/ M2 - VL - IS - SP - 1 EP - 8 UR - http://doi.acm.org/10.1145/2365934.2365936 M3 - 10.1145/2365934.2365936 KW - itegpub KW - extending KW - info20pub KW - myown KW - 2012 KW - folkrank KW - content L1 - SN - 978-1-4503-1638-5 N1 - Extending FolkRank with content data N1 - AB - 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. ER - TY - CONF AU - Landia, Nikolas AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Jäschke, Robert AU - Doerfel, Stephan AU - Mitzlaff, Folke A2 - T1 - Extending FolkRank with content data T2 - Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web PB - ACM CY - New York, NY, USA PY - 2012/ M2 - VL - IS - SP - 1 EP - 8 UR - http://doi.acm.org/10.1145/2365934.2365936 M3 - 10.1145/2365934.2365936 KW - itegpub KW - extending KW - 2012 KW - folkrank KW - content L1 - SN - 978-1-4503-1638-5 N1 - Extending FolkRank with content data N1 - AB - 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. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 M3 - KW - tagging KW - graph KW - recommender KW - collaborative KW - social KW - folksonomy KW - bookmarking KW - 2013 KW - myown KW - folkrank L1 - SN - N1 - N1 - AB - The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 M3 - KW - graph KW - collaborative KW - folksonomy KW - bookmarking KW - 2013 KW - myown KW - folkrank L1 - SN - N1 - N1 - AB - The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain. ER - TY - JOUR AU - Landia, Nikolas AU - Doerfel, Stephan AU - Jäschke, Robert AU - Anand, Sarabjot Singh AU - Hotho, Andreas AU - Griffiths, Nathan T1 - Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations JO - cs.IR PY - 2013/ VL - 1310.1498 IS - SP - EP - UR - http://arxiv.org/abs/1310.1498 M3 - KW - tagging KW - graph KW - itegpub KW - recommender KW - collaborative KW - social KW - l3s KW - folksonomy KW - bookmarking KW - 2013 KW - iteg KW - folkrank L1 - SN - N1 - N1 - AB - The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain. ER -