%0 Conference Paper %1 citeulike:7133323 %A Bao, Hongji %A Chang, Edward Y. %B WWW '10: Proceedings of the 19th international conference on World wide web %C New York, NY, USA %D 2010 %I ACM %K recommender, social-network, spread-activation %P 71--80 %R 10.1145/1772690.1772699 %T AdHeat: an influence-based diffusion model for propagating hints to match ads %U http://dx.doi.org/10.1145/1772690.1772699 %X In this paper, we present AdHeat, a social ad model considering user influence in addition to relevance for matching ads. Traditionally, ad placement employs the relevance model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. We performed experiments on a large online Q&\#38;A community with half a million users. The experimental results show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins. %@ 978-1-60558-799-8

@inproceedings{citeulike:7133323, abstract = {{In this paper, we present AdHeat, a social ad model considering user influence in addition to relevance for matching ads. Traditionally, ad placement employs the relevance model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. We performed experiments on a large online Q\&\#38;A community with half a million users. The experimental results show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins.}}, added-at = {2017-11-15T17:02:25.000+0100}, address = {New York, NY, USA}, author = {Bao, Hongji and Chang, Edward Y.}, biburl = {https://www.bibsonomy.org/bibtex/23652b0961f998e58e3e3d50e1e403ff9/brusilovsky}, booktitle = {WWW '10: Proceedings of the 19th international conference on World wide web}, citeulike-article-id = {7133323}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1772690.1772699}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1772690.1772699}, doi = {10.1145/1772690.1772699}, interhash = {b9e95ff59b78fcf2ebdc74ff4a4d6322}, intrahash = {3652b0961f998e58e3e3d50e1e403ff9}, isbn = {978-1-60558-799-8}, keywords = {recommender, social-network, spread-activation}, location = {Raleigh, North Carolina, USA}, pages = {71--80}, posted-at = {2010-06-23 19:54:10}, priority = {2}, publisher = {ACM}, timestamp = {2017-11-15T17:02:25.000+0100}, title = {{AdHeat: an influence-based diffusion model for propagating hints to match ads}}, url = {http://dx.doi.org/10.1145/1772690.1772699}, year = 2010 }