{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:34:24Z","timestamp":1778661264007,"version":"3.51.4"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,6]]},"abstract":"<jats:p>\n            Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the users and items into a smaller dimensional space, thereby clustering similar users and items together and subsequently compute similarity between unknown user-item pairs. When user-item interactions are sparse (\n            <jats:italic>sparsity<\/jats:italic>\n            problem) or when new items continuously appear (\n            <jats:italic>cold start<\/jats:italic>\n            problem), these models perform poorly. In this paper, we exploit the combination of taxonomies and latent factor models to mitigate these issues and improve recommendation accuracy. We observe that taxonomies provide structure similar to that of a latent factor model: namely, it imposes human-labeled categories (clusters) over items. This leads to our proposed\n            <jats:italic>taxonomy-aware<\/jats:italic>\n            latent factor model (TF) which combines taxonomies and latent factors using additive models. We develop efficient algorithms to train the TF models, which scales to large number of users\/items and develop scalable inference\/recommendation algorithms by exploiting the structure of the taxonomy. In addition, we extend the TF model to account for the temporal dynamics of user interests using high-order\n            <jats:italic>Markov chains<\/jats:italic>\n            . To deal with large-scale data, we develop a parallel multi-core implementation of our TF model. We empirically evaluate the TF model for the task of predicting user purchases using a real-world shopping dataset spanning more than a million users and products. Our experiments demonstrate the benefits of using our TF models over existing approaches, in terms of both prediction accuracy and running time.\n          <\/jats:p>","DOI":"10.14778\/2336664.2336669","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"956-967","source":"Crossref","is-referenced-by-count":46,"title":["Supercharging recommender systems using taxonomies for learning user purchase behavior"],"prefix":"10.14778","volume":"5","author":[{"given":"Bhargav","family":"Kanagal","sequence":"first","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amr","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandeep","family":"Pandey","sequence":"additional","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanja","family":"Josifovski","sequence":"additional","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeff","family":"Yuan","sequence":"additional","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lluis","family":"Garcia-Pueyo","sequence":"additional","affiliation":[{"name":"Yahoo! Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Boost c++ libraries. http:\/\/www.boost.org\/.  Boost c++ libraries. http:\/\/www.boost.org\/."},{"key":"e_1_2_1_2_1","unstructured":"Kdd cup 2011. http:\/\/kddcup.yahoo.com\/.  Kdd cup 2011. http:\/\/kddcup.yahoo.com\/."},{"key":"e_1_2_1_3_1","unstructured":"Pricegrabber. http:\/\/www.pricegrabber.com\/.  Pricegrabber. http:\/\/www.pricegrabber.com\/."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557029"},{"key":"e_1_2_1_5_1","first-page":"487","volume-title":"VLDB","author":"Agrawal R.","year":"1994","unstructured":"R. Agrawal and R. Srikant . Fast algorithms for mining association rules in large databases . In VLDB , pages 487 -- 499 , 1994 . R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB, pages 487--499, 1994."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/645480.655281"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020433"},{"key":"e_1_2_1_8_1","volume-title":"In KDD Cup and Workshop in conjunction with KDD","author":"Bennett J.","year":"2007","unstructured":"J. Bennett and S. Lanning . The netflix prize . In In KDD Cup and Workshop in conjunction with KDD , 2007 . J. Bennett and S. Lanning. The netflix prize. In In KDD Cup and Workshop in conjunction with KDD, 2007."},{"key":"e_1_2_1_9_1","first-page":"146","volume-title":"Advanced Lectures on Machine Learning","author":"Bottou L.","year":"2003","unstructured":"L. Bottou . Stochastic learning . In Advanced Lectures on Machine Learning , pages 146 -- 168 , 2003 . L. Bottou. Stochastic learning. In Advanced Lectures on Machine Learning, pages 146--168, 2003."},{"key":"e_1_2_1_10_1","volume-title":"NIPS","author":"Bottou L.","year":"2007","unstructured":"L. Bottou and O. Bousquet . The tradeoffs of large scale learning . In NIPS , 2007 . L. Bottou and O. Bousquet. The tradeoffs of large scale learning. In NIPS, 2007."},{"key":"e_1_2_1_11_1","volume-title":"NIPS","author":"Bottou L.","year":"2003","unstructured":"L. Bottou and Y. LeCun . Large scale online learning . In NIPS , 2003 . L. Bottou and Y. LeCun. Large scale online learning. In NIPS, 2003."},{"key":"e_1_2_1_12_1","doi-asserted-by":"crossref","DOI":"10.1201\/9780429258480","volume-title":"Bayesian Data Analysis","author":"Gelman A.","year":"2003","unstructured":"A. Gelman , J. B. Carlin , H. S. Stern , and D. B. Rubin . Bayesian Data Analysis . Chapman and Hall\/CRC Texts in Statistical Science, 2003 . A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall\/CRC Texts in Statistical Science, 2003."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2009.36"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0059-1"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/308574"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2043964"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557072"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_5"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020436"},{"key":"e_1_2_1_22_1","volume-title":"KDD Cup and Workshop, KDD 2011","author":"Mnih A.","year":"2011","unstructured":"A. Mnih . Taxonomy-informed latent factor models for implicit feedback . In KDD Cup and Workshop, KDD 2011 , 2011 . A. Mnih. Taxonomy-informed latent factor models for implicit feedback. In KDD Cup and Workshop, KDD 2011, 2011."},{"key":"e_1_2_1_23_1","volume-title":"Morgan Kaufmann","author":"Pearl J.","year":"1988","unstructured":"J. Pearl . Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference . Morgan Kaufmann , 1988 . J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988."},{"key":"e_1_2_1_24_1","first-page":"452","volume-title":"UAI","author":"Rendle S.","year":"2009","unstructured":"S. Rendle , C. Freudenthaler , Z. Gantner , and L. Schmidt-thieme. L. S. : Bpr: Bayesian personalized ranking from implicit feedback . In UAI , pages 452 -- 461 , 2009 . S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-thieme. L. S.: Bpr: Bayesian personalized ranking from implicit feedback. In UAI, pages 452--461, 2009."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718498"},{"key":"e_1_2_1_27_1","first-page":"407","volume-title":"VLDB","author":"Srikant R.","year":"1995","unstructured":"R. Srikant and R. Agrawal . Mining generalized association rules . In VLDB , pages 407 -- 419 , 1995 . R. Srikant and R. Agrawal. Mining generalized association rules. In VLDB, pages 407--419, 1995."},{"key":"e_1_2_1_28_1","first-page":"384","article-title":"Learning a parametric embedding by preserving local structure","volume":"5","author":"van der Maaten L.","year":"2009","unstructured":"L. van der Maaten . Learning a parametric embedding by preserving local structure . JMLR , 5 : 384 -- 391 , 2009 . L. van der Maaten. Learning a parametric embedding by preserving local structure. JMLR, 5: 384--391, 2009.","journal-title":"JMLR"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2008.97"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1031171.1031252"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2336664.2336669","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:45:39Z","timestamp":1672224339000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2336664.2336669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,6]]},"references-count":30,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2012,6]]}},"alternative-id":["10.14778\/2336664.2336669"],"URL":"https:\/\/doi.org\/10.14778\/2336664.2336669","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2012,6]]}}}