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WWW &apos;13, pp.1-12, 2013. 10.1145\/2488388.2488390","DOI":"10.1145\/2488388.2488390"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] P. Adamopoulos and A. Tuzhilin, \u201cOn over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems,\u201d Proc. RecSys&apos;14, pp.153-160, 2014. 10.1145\/2645710.2645752","DOI":"10.1145\/2645710.2645752"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] G. Adomavicius and Y. Kwon, \u201cImproving aggregate recommendation diversity using ranking-based techniques,\u201d IEEE Trans. Knowl. Data Eng., vol.24, no.5, pp.896-911, 2012. 10.1109\/tkde.2011.15","DOI":"10.1109\/TKDE.2011.15"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, \u201cDiversifying search results,\u201d Proc. the 2nd ACM Int&apos;l Conf. on Web Search and Data Mining, pp.5-14, 2009. 10.1145\/1498759.1498766","DOI":"10.1145\/1498759.1498766"},{"key":"5","unstructured":"[5] C. Anderson, \u201cThe long tail: why the future of business is selling less of more,\u201d Proc. Hyperion, New York, 2006."},{"key":"6","unstructured":"[6] A. Ashkan, B. Kveton, S. Berkovsky, and Z. Wen, \u201cOptimal greedy diversity for recommendation,\u201d Proc. the 24th Int&apos;l Conf. on AI, pp.1742-1748, 2015."},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] F.M. Bel\u00e9m, R. Santos, J. Almeida, and M.A. Gon\u00e7alves, \u201cTopic diversity in tag recommendation,\u201d Proc. RecSys&apos;13, pp.141-148, 2013. 10.1145\/2507157.2507184","DOI":"10.1145\/2507157.2507184"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] F.M. Bel\u00e9m, E.F. Martins, J.M. Almeida, and M.A. Gon\u00e7alves, \u201cExploiting novelty and diversity in tag recommendation,\u201d Proc. European Conf. on Info. Retrieval 2013, pp.380-391, 2013.","DOI":"10.1007\/978-3-642-36973-5_32"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] I. Benouaret and D. Lenne, \u201cA package recommendation framework for trip planning activities,\u201d Proc. RecSys&apos;16, pp.203-206, 2016. 10.1145\/2959100.2959183","DOI":"10.1145\/2959100.2959183"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] R. Boim, T. Milo, and S. Novgorodov, \u201cDiversification and refinement in collaborative filtering recommender,\u201d Proc. the 20th ACM Int&apos;l Conf. on Info. and knowledge management, pp.739-744, 2011. 10.1145\/2063576.2063684","DOI":"10.1145\/2063576.2063684"},{"key":"11","unstructured":"[11] K. Bradley and B. Smyth, \u201cImproving recommendation diversity,\u201d Proc. 12th Irish Conf. on AI and Cognitive Science, pp.85-94, 2001."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] D.G. Bridge and A. Ferguson, \u201cDiverse product recommendations using an expressive language for case retrieval,\u201d Proc. the 6th European Conf. on Advances in Case-Based Reasoning, pp.43-57, 2002.","DOI":"10.1007\/3-540-46119-1_5"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] J. Carbonell and J. Goldstein, \u201cThe use of MMR, diversity-based reranking for reordering documents and producing summaries,\u201d Proc. SIGIR&apos;98, pp.335-336, 1998. 10.1145\/290941.291025","DOI":"10.1145\/290941.291025"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] B. Carterette, \u201cAn analysis of NP-completeness in novelty and diversity ranking,\u201d Proc. Info. Retr., vol.14, no.1, pp.89-106, 2011. 10.1007\/s10791-010-9157-1","DOI":"10.1007\/s10791-010-9157-1"},{"key":"15","unstructured":"[15] P. Castells and S. Vargas, \u201cNovelty and diversity metrics for recommender systems: choice, discovery and relevance,\u201d Proc. Int&apos;l Workshop on Diversity in Document Retrieval, pp.29-37, 2011."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] \u00d3. Celma and P. Herrera, \u201cA new approach to evaluating novel recommendations,\u201d Proc. RecSys&apos;08, pp.179-186, 2008. 10.1145\/1454008.1454038","DOI":"10.1145\/1454008.1454038"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] E. Chatzicharalampous, Z. Christos, and A. Vakali, \u201cExploriometer: leveraging personality traits for coverage and diversity aware recommendations,\u201d Proc. WWW&apos;15 Companion, pp.1463-1468, 2015. 10.1145\/2740908.2742140","DOI":"10.1145\/2740908.2742140"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] F. Christoffel, B. Paudel, C. Newell, and A. Bernstein, \u201cBlockbusters and wallflowers: accurate, diverse, and scalable recommendations with random walks,\u201d Proc. RecSys&apos;15, pp.163-170, 2015. 10.1145\/2792838.2800180","DOI":"10.1145\/2792838.2800180"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] C.L.A. Clarke, M. Kolla, G.V. Cormack, O. Vechtomova, A. Ashkan, S. B\u00fcttcher, and I. MacKinnon, \u201cNovelty and diversity in information retrieval evaluation,\u201d Proc. SIGIR&apos;08, pp.659-666, 2008. 10.1145\/1390334.1390446","DOI":"10.1145\/1390334.1390446"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] P. Cremonesi, F. Garzotto, R. Pagano, and M. Quadrana, \u201cRecommending without short head,\u201d Proc. WWW&apos;14 Companion, pp.245-246, 2014. 10.1145\/2567948.2577286","DOI":"10.1145\/2567948.2577286"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M.D. Ekstrand, F.M. Harper, M.C. Willemsen, and J.A. Konstan, \u201cUser perception of differences in recommender algorithms,\u201d Proc. RecSys&apos;14, pp.161-168, 2014. 10.1145\/2645710.2645737","DOI":"10.1145\/2645710.2645737"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] D.M. Fleder and K. Hosanagar, \u201cRecommender systems and their impact on sales diversity,\u201d Proc. the 8th ACM Conf. on Electronic commerce, pp.192-199, 2007. 10.1145\/1250910.1250939","DOI":"10.1145\/1250910.1250939"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] D. Fleder and K. Hosanagar, \u201cBlockbuster culture&apos;s next rise or fall: the impact of recommender systems on sales diversity,\u201d Proc. Management Science, vol.55, no.5, pp.697-712, 2009. 10.1287\/mnsc.1080.0974","DOI":"10.1287\/mnsc.1080.0974"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] D. Fogaras, B. R\u00e1cz, K. Csalog\u00e1ny, and T. Sarl\u00f3s, \u201cTowards scaling fully personalized PageRank: algorithms, lower bounds, and experiments,\u201d Proc. Internet Mathematics, vol.2, no.3, pp.333-358, 2005. 10.1080\/15427951.2005.10129104","DOI":"10.1080\/15427951.2005.10129104"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] F. Garcin, B. Faltings, O. Donatsch, A. Alazzawi, C. Bruttin, and A. Huber, \u201cOffline and online evaluation of news recommender systems at swissinfo.ch,\u201d Proc. of RecSys&apos;14, pp.169-176, 2014. 10.1145\/2645710.2645745","DOI":"10.1145\/2645710.2645745"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] M. Ge, C. Delgado-Battenfeld, and D. Jannach, \u201cBeyond accuracy: evaluating recommender systems by coverage and serendipity,\u201d Proc. RecSys&apos;10, pp.257-260, 2010. 10.1145\/1864708.1864761","DOI":"10.1145\/1864708.1864761"},{"key":"27","unstructured":"[27] C. Gini, \u201cConcentration and dependency ratios (1909 in Italian),\u201d Proc. English translation in Rivista di Politica Economica, vol.87, pp.769-789, 1997."},{"key":"28","unstructured":"[28] A. Gunawardana and G. Shani, \u201cA survey of accuracy evaluation metrics of recommendation tasks,\u201d The Journal of Machine Learning Research, vol.10, pp.2935-2962, 2009."},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, \u201cEvaluating collaborative filtering recommender systems,\u201d Proc. ACM Trans. Info. Systems, vol.22, no.1, pp.5-53, 2004. 10.1145\/963770.963772","DOI":"10.1145\/963770.963772"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] Y. Hijikata, T. Shimizu, and S. Nishida, \u201cDiscovery-oriented collaborative filtering for improving user satisfaction,\u201d Proc. the 14th Int&apos;l Conf. on Intell. user interfaces, pp.67-76, 2009.","DOI":"10.1145\/1502650.1502663"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] N. Hurley and M. Zhang, \u201cNovelty and diversity in top-n recommendation-analysis and evaluation,\u201d ACM Trans. Internet Technol., vol.10, no.4, pp.14:11-14:30, 2011.","DOI":"10.1145\/1944339.1944341"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] N.J. Hurley, \u201cPersonalised ranking with diversity,\u201d Proc. RecSys&apos;13, pp.379-382, 2013. 10.1145\/2507157.2507226","DOI":"10.1145\/2507157.2507226"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] M. Ishikawa, P. Geczy, N. Izumi, and T. Yamaguchi, \u201cLong tail recommender utilizing information diffusion theory,\u201d Proc. the 2008 IEEE\/WIC\/ACM Int&apos;l Conf. on Web Intell. and Intell. Agent Technol., vol.01, pp.785-788, 2008. 10.1109\/wiiat.2008.352","DOI":"10.1109\/WIIAT.2008.352"},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] T. Jambor and J. Wang, \u201cOptimizing multiple objectives in collaborative filtering,\u201d Proc. RecSys&apos;10, pp.55-62, 2010. 10.1145\/1864708.1864723","DOI":"10.1145\/1864708.1864723"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] K. J\u00e4rvelin and J. Kek\u00e4l\u00e4inen, \u201cCumulated gain-based evaluation of ir techniques,\u201d ACM Transactions on Information Systems, vol.20, no.4, pp.422-446, 2002. 10.1145\/582415.582418","DOI":"10.1145\/582415.582418"},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] K. Kapoor, V. Kumar, L. Terveen, J.A. Konstan, and P. Schrater, \u201cI like to explore sometimes: adapting to dynamic user novelty preferences,\u201d Proc. RecSys&apos;15, pp.19-26, 2015. 10.1145\/2792838.2800172","DOI":"10.1145\/2792838.2800172"},{"key":"37","unstructured":"[37] P. Kohli, M. Salek, and G. Stoddard, \u201cA fast bandit algorithm for recommendations to users with heterogeneous tastes,\u201d Proc. the 27th AAAI Conf. on AI., pp.1135-1141, AAAI Press, 2013."},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] O. K\u00fc\u00e7\u00fcktun\u00e7, E. Saule, K. Kaya, and \u00dc.V. \u00c7ataly\u00fcrek, \u201cDiversified recommendation on graphs: pitfalls, measures, and algorithms,\u201d Proc. WWW&apos;13, pp.715-726, 2013. 10.1145\/2488388.2488451","DOI":"10.1145\/2488388.2488451"},{"key":"39","doi-asserted-by":"publisher","unstructured":"[39] O. K\u00fc\u00e7\u00fcktun\u00e7, E. Saule, K. Kaya, and \u00dc.V. \u00c7ataly\u00fcrek, \u201cDiversifying citation recommendations,\u201d ACM Trans. Intell. Syst. Technol., vol.5, no.4, article 55, 2015. 10.1145\/2668106","DOI":"10.1145\/2668106"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] N. Lathia, S. Hailes, L. Capra, and X. Amatriain, \u201cTemporal diversity in recommender systems,\u201d Proc. SIGIR &apos;10, pp.210-217, 2010. 10.1145\/1835449.1835486","DOI":"10.1145\/1835449.1835486"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] K. Lee and K. Lee, \u201cMy head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation,\u201d Proc. RecSys&apos;11, pp.213-220, 2011. 10.1145\/2043932.2043971","DOI":"10.1145\/2043932.2043971"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] L. Li, D. Wang, T. Li, D. Knox, and B. Padmanabhan, \u201cSCENE: a scalable two-stage personalized news recommendation system,\u201d Proc. SIGIR &apos;11, pp.125-134, 2011. 10.1145\/2009916.2009937","DOI":"10.1145\/2009916.2009937"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] X. Li and T. Murata, \u201cMultidimensional clustering based collaborative filtering approach for diversified recommendation,\u201d Proc. 7th Int&apos;l Conf. on Computer Science &amp; Education, pp.905-910, 2012.","DOI":"10.1109\/ICCSE.2012.6295214"},{"key":"44","unstructured":"[44] H. Ma, M.R. Lyu, and I. King, \u201cDiversifying query suggestion results,\u201d Proc. the 24th AAAI Conf. on AI, pp.1399-1404, 2010."},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] S.M. McNee, J. Riedl, and J.A. Konstan, \u201cBeing accurate is not enough: how accuracy metrics have hurt recommender systems,\u201d Proc. CHI &apos;06 Extended Abstracts on Human Factors in Computing Systems, pp.1097-1101, 2006. 10.1145\/1125451.1125659","DOI":"10.1145\/1125451.1125659"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] F. Mour\u00e3o, L. Rocha, J.A. Konstan, and W. Meira, Jr., \u201cExploiting non-content preference attributes through hybrid recommendation method,\u201d Proc. RecSys&apos;13, pp.177-184, 2013. 10.1145\/2507157.2507179","DOI":"10.1145\/2507157.2507179"},{"key":"47","doi-asserted-by":"crossref","unstructured":"[47] T. Murakami, K. Mori, and R. Orihara, \u201cMetrics for evaluating the serendipity of recommendation lists,\u201d Proc. the 2007 Conf. on New frontiers in AI, pp.40-46, 2007.","DOI":"10.1007\/978-3-540-78197-4_5"},{"key":"48","doi-asserted-by":"crossref","unstructured":"[48] T.T. Nguyen, P.-M. Hui, F.M. Harper, L. Terveen, and J.A. Konstan, \u201cExploring the filter bubble: the effect of using recommender systems on content diversity,\u201d Proc. WWW&apos;14, pp.677-686, 2014. 10.1145\/2566486.2568012","DOI":"10.1145\/2566486.2568012"},{"key":"49","doi-asserted-by":"crossref","unstructured":"[49] K. Niemann and M. Wolpers, \u201cA new collaborative filtering approach for increasing the aggregate diversity of recommender systems,\u201d Proc. KDD&apos;13, pp.955-963, 2013. 10.1145\/2487575.2487656","DOI":"10.1145\/2487575.2487656"},{"key":"50","doi-asserted-by":"crossref","unstructured":"[50] T.D. Noia, V.C. Ostuni, J. Rosati, P. Tomeo, and E.D. Sciascio, \u201cAn analysis of users&apos; propensity toward diversity in recommendations,\u201d Proc. RecSys&apos;14, pp.285-288, 2014. 10.1145\/2645710.2645774","DOI":"10.1145\/2645710.2645774"},{"key":"51","doi-asserted-by":"crossref","unstructured":"[51] J. Oh, S. Park, H. Yu, M. Song, and S.-T. Park, \u201cNovel recommendation based on personal popularity tendency,\u201d Proc. the 2011 IEEE 11th Int&apos;l Conf. on Data Mining, pp.507-516, 2011. 10.1109\/icdm.2011.110","DOI":"10.1109\/ICDM.2011.110"},{"key":"52","doi-asserted-by":"publisher","unstructured":"[52] U. Panniello, A. Tuzhilin, and M. Gorgoglione, \u201cComparing context-aware recommender systems in terms of accuracy and diversity,\u201d User Modeling and User-Adapted Interaction, vol.24, no.1, pp.35-65, 2014. 10.1007\/s11257-012-9135-y","DOI":"10.1007\/s11257-012-9135-y"},{"key":"53","doi-asserted-by":"crossref","unstructured":"[53] S.A.P. Parambath, N. Usunier, and Y. Grandvalet, \u201cA coverage-based approach to recommendation diversity on similarity graph,\u201d Proc. RecSys&apos;16, pp.15-22, 2016. 10.1145\/2959100.2959149","DOI":"10.1145\/2959100.2959149"},{"key":"54","doi-asserted-by":"crossref","unstructured":"[54] Y. Park and A. Tuzhilin, \u201cThe long tail of recommender systems and how to leverage it,\u201d Proc. RecSys&apos;08, pp.11-18, 2008. 10.1145\/1454008.1454012","DOI":"10.1145\/1454008.1454012"},{"key":"55","doi-asserted-by":"publisher","unstructured":"[55] P. Pu, L. Chen, and R. Hu, \u201cEvaluating recommender systems from the user&apos;s perspective: survey of the state of the art,\u201d User Modeling and User-Adapted Interaction, vol.22, no.4-5, pp.317-355, 2012. 10.1007\/s11257-011-9115-7","DOI":"10.1007\/s11257-011-9115-7"},{"key":"56","unstructured":"[56] L. Qin and X. Zhu, \u201cPromoting diversity in recommendation by entropy regularizer,\u201d Proc. the 23rd Int&apos;l joint Conf. on AI, pp.2698-2704, 2013."},{"key":"57","doi-asserted-by":"publisher","unstructured":"[57] F. Radlinski, P.N. Bennett, B. Carterette, and T. Joachims, \u201cRedundancy, diversity and interdependent document relevance,\u201d Proc.SIGIR Forum 43, pp.46-52, 2009. 10.1145\/1670564.1670572","DOI":"10.1145\/1670564.1670572"},{"key":"58","doi-asserted-by":"crossref","unstructured":"[58] M.T. Ribeiro, A. Lacerda, A. Veloso, and N. Ziviani, \u201cPareto-efficient hybridization for multi-objective recommender systems,\u201d Proc. RecSys&apos;12, pp.19-26, 2012. 10.1145\/2365952.2365962","DOI":"10.1145\/2365952.2365962"},{"key":"59","doi-asserted-by":"crossref","unstructured":"[59] F. Ricci, L. Rokach, B. Shapira, and P.B. Kantor, Recommender systems handbook, Springer-Verlag New York, Inc., USA, 2010.","DOI":"10.1007\/978-0-387-85820-3"},{"key":"60","doi-asserted-by":"crossref","unstructured":"[60] M. Rossetti, F. Stella, and M. Zanker, \u201cContrasting offline and online results when evaluating recommendation algorithms,\u201d Proc. RecSys&apos;16, pp.31-34, 2016. 10.1145\/2959100.2959176","DOI":"10.1145\/2959100.2959176"},{"key":"61","doi-asserted-by":"crossref","unstructured":"[61] A. Said, B. Fields, B.J. Jain, and S. Albayrak, \u201cUser-centric evaluation of a k-furthest neighbor collaborative filtering recommender algorithm,\u201d Proc. the 2013 Conf. on Computer supported cooperative work, pp.1399-1408, 2013. 10.1145\/2441776.2441933","DOI":"10.1145\/2441776.2441933"},{"key":"62","doi-asserted-by":"crossref","unstructured":"[62] R.L.T. Santos, C. Macdonald, and I. Ounis, \u201cExploiting query reformulations for web search result diversification,\u201d Proc. WWW&apos;10, pp.881-890, 2010. 10.1145\/1772690.1772780","DOI":"10.1145\/1772690.1772780"},{"key":"63","doi-asserted-by":"crossref","unstructured":"[63] M. Servajean, E. Pacitti, S. Amer-Yahia, and P. Neveu, \u201cProfile diversity in search and recommendation,\u201d Proc. WWW&apos;13 Companion, pp.973-980, 2013. 10.1145\/2487788.2488094","DOI":"10.1145\/2487788.2488094"},{"key":"64","doi-asserted-by":"crossref","unstructured":"[64] L. Shi, \u201cTrading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach,\u201d Proc. RecSys&apos;13, pp.57-64, 2013. 10.1145\/2507157.2507165","DOI":"10.1145\/2507157.2507165"},{"key":"65","doi-asserted-by":"crossref","unstructured":"[65] K. Shi and K. Ali, \u201cGetJar mobile application recommendations with very sparse datasets,\u201d Proc. KDD&apos;12, pp.204-212, 2012. 10.1145\/2339530.2339563","DOI":"10.1145\/2339530.2339563"},{"key":"66","doi-asserted-by":"crossref","unstructured":"[66] Y. Shi, X. Zhao, J. Wang, M. Larson, and A. Hanjalic, \u201cAdaptive diversification of recommendation results via latent factor portfolio,\u201d Proc. SIGIR&apos;12, pp.175-184, 2012. 10.1145\/2348283.2348310","DOI":"10.1145\/2348283.2348310"},{"key":"67","doi-asserted-by":"crossref","unstructured":"[67] H. Steck, \u201cItem popularity and recommendation accuracy,\u201d Proc. RecSys&apos;11, pp.125-132, 2011. 10.1145\/2043932.2043957","DOI":"10.1145\/2043932.2043957"},{"key":"68","doi-asserted-by":"crossref","unstructured":"[68] R. Su, L. Yin, K. Chen, and Y. Yu, \u201cSet-oriented personalized ranking for diversified top-n recommendation,\u201d Proc. RecSys&apos;13, pp.415-418, 2013. 10.1145\/2507157.2507207","DOI":"10.1145\/2507157.2507207"},{"key":"69","unstructured":"[69] K. Swearingen and R. Sinha, \u201cBeyond Algorithms: An HCI Perspective on Recommender Systems,\u201d Proc. SIGIR workshop 2001, pp.393-408, 2001."},{"key":"70","doi-asserted-by":"crossref","unstructured":"[70] I. Szpektor, Y. Maarek, and D. Pelleg, \u201cWhen relevance is not enough: promoting diversity and freshness in personalized question recommendation,\u201d Proc. WWW&apos;13, pp.1249-1260, 2013. 10.1145\/2488388.2488497","DOI":"10.1145\/2488388.2488497"},{"key":"71","doi-asserted-by":"crossref","unstructured":"[71] M. Taramigkou, E. Bothos, K. Christidis, D. Apostolou, and G. Mentzas, \u201cEscape the bubble: guided exploration of music preferences for serendipity and novelty,\u201d Proc. RecSys&apos;13, pp.335-338, 2013. 10.1145\/2507157.2507223","DOI":"10.1145\/2507157.2507223"},{"key":"72","doi-asserted-by":"crossref","unstructured":"[72] C.H. Teo, H. Nassif, D. Hill, S. Srinivasan, M. Goodman, V. Mohan, and S.V.N. Vishwanathan, \u201cAdaptive, personalized diversity for visual discovery,\u201d Proc. RecSys&apos;16, pp.35-38, 2016. 10.1145\/2959100.2959171","DOI":"10.1145\/2959100.2959171"},{"key":"73","doi-asserted-by":"crossref","unstructured":"[73] I. Fern\u00e1ndez-Tob\u00edas, P. Tomeo, I. Cantador, T.D. Noia, and E.D.Sciascio, \u201cAccuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback,\u201d Proc. RecSys&apos;16, pp.119-122, 2016. 10.1145\/2959100.2959175","DOI":"10.1145\/2959100.2959175"},{"key":"74","doi-asserted-by":"crossref","unstructured":"[74] H. Tong, J. He, Z. Wen, R.i Konuru, and C.-Y. Lin, \u201cDiversified ranking on large graphs: an optimization viewpoint,\u201d Proc. KDD&apos;11, pp.1028-1036, 2011. 10.1145\/2020408.2020573","DOI":"10.1145\/2020408.2020573"},{"key":"75","doi-asserted-by":"crossref","unstructured":"[75] S. Vargas and P. Castells, \u201cRank and relevance in novelty and diversity metrics for recommender systems,\u201d Proc. RecSys&apos;11, pp.109-116, 2011. 10.1145\/2043932.2043955","DOI":"10.1145\/2043932.2043955"},{"key":"76","doi-asserted-by":"crossref","unstructured":"[76] S. Vargas, P. Castells, and D. Vallet, \u201cIntent-oriented diversity in recommender systems,\u201d Proc. SIGIR&apos;11, pp.1211-1212, 2011. 10.1145\/2009916.2010124","DOI":"10.1145\/2009916.2010124"},{"key":"77","unstructured":"[77] S. Vargas and P. Castells, \u201cExploiting the diversity of user preferences for recommendation,\u201d Proc. the 10th Conf. on Open Research Areas in Info. Retrieval, pp.129-136, 2013."},{"key":"78","doi-asserted-by":"crossref","unstructured":"[78] S. Vargas and P. Castells, \u201cImproving sales diversity by recommending users to items,\u201d Proc. RecSys&apos;14, pp.145-152, 2014. 10.1145\/2645710.2645744","DOI":"10.1145\/2645710.2645744"},{"key":"79","doi-asserted-by":"crossref","unstructured":"[79] S. Vargas, L. Baltrunas, A. Karatzoglou, and P. Castells, \u201cCoverage, redundancy and size-awareness in genre diversity for recommender systems,\u201d Proc. RecSys&apos;14, pp.209-216, 2014. 10.1145\/2645710.2645743","DOI":"10.1145\/2645710.2645743"},{"key":"80","doi-asserted-by":"crossref","unstructured":"[80] J. Wang and J. Zhu, \u201cPortfolio theory of information retrieval,\u201d Proc. SIGIR&apos;09, pp.115-122, 2009. 10.1145\/1571941.1571963","DOI":"10.1145\/1571941.1571963"},{"key":"81","doi-asserted-by":"crossref","unstructured":"[81] C. Wartena and M. Wibbels, \u201cImproving tag-based recommendation by topic diversification,\u201d Proc. the 33rd European Conf. on Advances in info. retrieval, pp.43-54, 2011.","DOI":"10.1007\/978-3-642-20161-5_7"},{"key":"82","doi-asserted-by":"crossref","unstructured":"[82] J. Wasilewski and N. Hurley, \u201cIntent-aware diversification using a constrained PLSA,\u201d Proc. RecSys&apos;16, pp.39-42, 2016. 10.1145\/2959100.2959177","DOI":"10.1145\/2959100.2959177"},{"key":"83","doi-asserted-by":"crossref","unstructured":"[83] L.-T. Weng, Y. Xu, Y. Li, and R. Nayak, \u201cImproving recommendation novelty based on topic taxonomy,\u201d Proc. Workshops on Web Intell. and Intell. Agent Technol., on IEEE\/WIC\/ACM Int&apos;l Conf., pp.115-118, 2007.","DOI":"10.1109\/WIIATW.2007.4427553"},{"key":"84","doi-asserted-by":"publisher","unstructured":"[84] L. Wu, Q. Liu, E. Chen, N.J. Yuan, G. Guo, and X. Xie, \u201cRelevance meets coverage: a unified framework to generate diversified recommendations,\u201d ACM Trans. Intell. Syst. Technol., vol.7, no.3, Article 39, 2016. 10.1145\/2700496","DOI":"10.1145\/2700496"},{"key":"85","doi-asserted-by":"publisher","unstructured":"[85] H. Yin, B. Cui, J. Li, J. Yao, and C. Chen, \u201cChallenging the long tail recommendation,\u201d Proc. VLDB Endow., vol.5, no.9, pp.896-907, 2012. 10.14778\/2311906.2311916","DOI":"10.14778\/2311906.2311916"},{"key":"86","unstructured":"[86] C. Yu, L. Lakshmanan, and S. Amer-Yahia, \u201cIt takes variety to make a world: diversification in recommender systems,\u201d Proc. the 12th Int&apos;l Conf. on Extending DB Technol.: Advances in DB Technol., pp.368-378, 2009. 10.1145\/1516360.1516404"},{"key":"87","doi-asserted-by":"crossref","unstructured":"[87] C.X. Zhai, W.W. Cohen, and J. Lafferty, \u201cBeyond independent relevance: methods and evaluation metrics for subtopic retrieval,\u201d Proc. SIGIR&apos;03, pp.10-17, 2003. 10.1145\/860438.860440","DOI":"10.1145\/860435.860440"},{"key":"88","doi-asserted-by":"crossref","unstructured":"[88] M. Zhang and N. Hurley, \u201cNovel item recommendation by user profile partitioning,\u201d Proc. of the 2009 IEEE\/WIC\/ACM Int&apos;l Conf. on Web Intell. and Intell. Agent Technol., vol.01, pp.508-515, 2009. 10.1109\/wi-iat.2009.85","DOI":"10.1109\/WI-IAT.2009.85"},{"key":"89","doi-asserted-by":"crossref","unstructured":"[89] M. Zhang and N. Hurley, \u201cEvaluating the diversity of top-n recommendations,\u201d Proc. the 21st Int&apos;l Conf. on Tools with AI, pp.457-460, 2009.","DOI":"10.1109\/ICTAI.2009.46"},{"key":"90","doi-asserted-by":"crossref","unstructured":"[90] M. Zhang and N. Hurley, \u201cStatistical modeling of diversity intop-n recommender systems,\u201d Proc. of the 2009 IEEE\/WIC\/ACM Int&apos;l Joint Conf. on Web Intell. and Intell. Agent Technol., vol.1, pp.490-497, 2009. 10.1109\/wi-iat.2009.83","DOI":"10.1109\/WI-IAT.2009.83"},{"key":"91","doi-asserted-by":"crossref","unstructured":"[91] M. Zhang and N. Hurley, \u201cNiche product retrieval in top-n recommendation,\u201d Proc. the 2010 IEEE\/WIC\/ACM Int&apos;l Conf. on Web Intell. and Intell. Agent Technol., pp.74-81, 2010. 10.1109\/wi-iat.2010.79","DOI":"10.1109\/WI-IAT.2010.79"},{"key":"92","doi-asserted-by":"crossref","unstructured":"[92] Y.C. Zhang, D.\u00d3. S\u00e9aghdha, D. Quercia, and T. Jambor, \u201cAuralist: introducing serendipity into music recommendation,\u201d Proc. the 5th ACM Int&apos;l Conf. on Web search and data mining, pp.13-22, 2012. 10.1145\/2124295.2124300","DOI":"10.1145\/2124295.2124300"},{"key":"93","doi-asserted-by":"crossref","unstructured":"[93] X. Zhao, Z. Niu, and W. Chen, \u201cOpinion-based collaborative filtering to solve popularity bias in recommender systems,\u201d Proc. Int&apos;l Conf. on Database and Expert Systems Applications, pp.426-433, 2013.","DOI":"10.1007\/978-3-642-40173-2_35"},{"key":"94","doi-asserted-by":"crossref","unstructured":"[94] T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, and Y.-C. Zhang, \u201cSolving the apparent diversity-accuracy dilemma of recommender systems,\u201d Proc. PNAS, vol.107, no.10, pp.4511-4515, 2010. 10.1073\/pnas.1000488107","DOI":"10.1073\/pnas.1000488107"},{"key":"95","unstructured":"[95] X. Zhu, A.B. Goldberg, J.V. Gael, and D. Andrezejewski, \u201cImproving diversity in ranking using absorbing random walks,\u201d Proc. HLT-NAACL, pp.97-104, 2007."},{"key":"96","doi-asserted-by":"crossref","unstructured":"[96] C.N. Ziegler, S.M. McNee, J.A. Konstan, and G. Lausen, \u201cImproving recommendation lists through topic diversification,\u201d Proc. the 14th Int&apos;l Conf. on World Wide Web, pp.22-32, 2005. 10.1145\/1060745.1060754","DOI":"10.1145\/1060745.1060754"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/12\/E100.D_2017EDR0003\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T22:17:49Z","timestamp":1751062669000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E100.D\/12\/E100.D_2017EDR0003\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":96,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2017edr0003","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}