Travelled to:1 × Austria
1 × Canada
1 × Germany
1 × Ireland
1 × Portugal
1 × South Korea
1 × Spain
11 × USA
2 × China
2 × Switzerland
Collaborated with:R.D.Burke J.Cleland-Huang N.Hariri C.Castro-Herrera C.Duan Y.Zheng J.Gemmell X.Jin Y.Zhou X.Amatriain T.Schimoler H.Dumitru A.Sieg J.J.Sandvig M.H.Aghdam A.Shepitsen C.Williams R.Bhaumik C.McMillan D.Poshyvanyk M.Ramezani L.Christiansen M.Gibiec M.Mirakhorli
Talks about:recommend (20) system (8) requir (5) base (5) context (4) mine (4) use (4) contextu (3) collabor (3) softwar (3)
Person: Bamshad Mobasher
DBLP: Mobasher:Bamshad
Facilitated 2 volumes:
EdEdContributed to:
20152015
2014
20142014
20142013
2012201220112010
20092009200920082008200820072007200620052004Wrote 24 papers:
- REFSQ-2015-DuanDCM #clustering #online #requirements
- User-Constrained Clustering in Online Requirements Forums (CD, HD, JCH, BM), pp. 284–299.
- RecSys-2015-AghdamHMB #adaptation #markov #modelling #recommendation #using
- Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models (MHA, NH, BM, RDB), pp. 241–244.
- CIKM-2014-ZhengMB #recommendation
- Deviation-Based Contextual SLIM Recommenders (YZ, BM, RDB), pp. 271–280.
- KDD-2014-AmatriainM #problem #recommendation #tutorial
- The recommender problem revisited: morning tutorial (XA, BM), p. 1971.
- RecSys-2014-HaririMB #adaptation #interactive #recommendation
- Context adaptation in interactive recommender systems (NH, BM, RDB), pp. 41–48.
- RecSys-2014-ZhengMB #algorithm #named #recommendation
- CSLIM: contextual SLIM recommendation algorithms (YZ, BM, RDB), pp. 301–304.
- SAC-2014-ZhengBM #empirical #recommendation
- Splitting approaches for context-aware recommendation: an empirical study (YZ, RDB, BM), pp. 274–279.
- RecSys-2013-HaririMB #recommendation
- Query-driven context aware recommendation (NH, BM, RDB), pp. 9–16.
- ICSE-2012-McMillanHPCM #agile #prototype #recommendation #source code
- Recommending source code for use in rapid software prototypes (CM, NH, DP, JCH, BM), pp. 848–858.
- RecSys-2012-HaririMB #music #recommendation #topic
- Context-aware music recommendation based on latenttopic sequential patterns (NH, BM, RDB), pp. 131–138.
- ICSE-2011-DumitruGHCMCM #mining #on-demand #recommendation
- On-demand feature recommendations derived from mining public product descriptions (HD, MG, NH, JCH, BM, CCH, MM), pp. 181–190.
- CIKM-2010-GemmellSMB #hybrid #recommendation #social
- Hybrid tag recommendation for social annotation systems (JG, TS, BM, RDB), pp. 829–838.
- RE-2009-Castro-HerreraCM #elicitation #online #recommendation #requirements
- Enhancing Stakeholder Profiles to Improve Recommendations in Online Requirements Elicitation (CCH, JCH, BM), pp. 37–46.
- RecSys-2009-Castro-HerreraCM #evolution #online #recommendation
- A recommender system for dynamically evolving online forums (CCH, JCH, BM), pp. 213–216.
- RecSys-2009-GemmellRSCM #ambiguity #folksonomy #recommendation
- The impact of ambiguity and redundancy on tag recommendation in folksonomies (JG, MR, TS, LC, BM), pp. 45–52.
- SAC-2009-Castro-HerreraDCM #elicitation #recommendation #requirements #scalability
- A recommender system for requirements elicitation in large-scale software projects (CCH, CD, JCH, BM), pp. 1419–1426.
- CIKM-2008-DuanCM #approach #clustering #requirements
- A consensus based approach to constrained clustering of software requirements (CD, JCH, BM), pp. 1073–1082.
- RE-2008-Castro-HerreraDCM #data mining #elicitation #mining #process #recommendation #requirements #scalability #using
- Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes (CCH, CD, JCH, BM), pp. 165–168.
- RecSys-2008-ShepitsenGMB #clustering #personalisation #recommendation #social #using
- Personalized recommendation in social tagging systems using hierarchical clustering (AS, JG, BM, RDB), pp. 259–266.
- CIKM-2007-SiegMB #ontology #personalisation #web
- Web search personalization with ontological user profiles (AS, BM, RDB), pp. 525–534.
- RecSys-2007-SandvigMB #collaboration #mining #recommendation #robust
- Robustness of collaborative recommendation based on association rule mining (JJS, BM, RDB), pp. 105–112.
- KDD-2006-BurkeMWB #classification #collaboration #detection #recommendation
- Classification features for attack detection in collaborative recommender systems (RDB, BM, CW, RB), pp. 542–547.
- KDD-2005-JinZM #collaboration #recommendation #web
- A maximum entropy web recommendation system: combining collaborative and content features (XJ, YZ, BM), pp. 612–617.
- KDD-2004-JinZM #analysis #mining #probability #semantics #web
- Web usage mining based on probabilistic latent semantic analysis (XJ, YZ, BM), pp. 197–205.