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Volumn , Issue , 2015, Pages 1373-1383

Daily-Aware personalized recommendation based on feature-level time series analysis

Author keywords

Collaborative Filtering; Recommender Systems; Sentiment Analysis; Time Series Analysis

Indexed keywords

COLLABORATIVE FILTERING; ELECTRONIC COMMERCE; HARMONIC ANALYSIS; RECOMMENDER SYSTEMS; SALES; WORLD WIDE WEB;

EID: 84968879562     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2736277.2741087     Document Type: Conference Paper
Times cited : (73)

References (40)
  • 2
    • 84866022799 scopus 로고    scopus 로고
    • Towards Time Dependant Recommendation based on Implicit Feedback
    • L. Baltrunas and X. Amatriain. Towards Time Dependant Recommendation based on Implicit Feedback. CARS, 2009.
    • (2009) CARS
    • Baltrunas, L.1    Amatriain, X.2
  • 5
    • 84894047367 scopus 로고    scopus 로고
    • Time-Aware recommender systems: A comprehensive survey and analysis of existing evaluation protocols
    • P. G. Campos, F. Dez, and I. Cantador. Time-Aware Recommender Systems: A Comprehensive Survey and Analysis of Existing Evaluation Protocols. User modeling & user-Adapted interaction, 24:67-119, 2014.
    • (2014) User Modeling & User-Adapted Interaction , vol.24 , pp. 67-119
    • Campos, P.G.1    Dez, F.2    Cantador, I.3
  • 6
    • 38049083830 scopus 로고    scopus 로고
    • Content recommendation system based on private dynamic user profiling
    • T. Chen, W. Han, H. Wang, Y. Zhou, B. Xu, and B. Zang. Content Recommendation System based on Private Dynamic User Profiling. ICMLC, 2007.
    • (2007) ICMLC
    • Chen, T.1    Han, W.2    Wang, H.3    Zhou, Y.4    Xu, B.5    Zang, B.6
  • 7
    • 84883091912 scopus 로고    scopus 로고
    • Modeling user's receptiveness over time for recommendation
    • W. Chen, W. Hsu, and M. Lee. Modeling User's Receptiveness Over Time for Recommendation. SIGIR, pages 373-382, 2013.
    • (2013) SIGIR , pp. 373-382
    • Chen, W.1    Hsu, W.2    Lee, M.3
  • 8
    • 84862835806 scopus 로고    scopus 로고
    • Predicting the present with google trends
    • H. Choi and H. Varian. Predicting the Present with Google Trends. Economic Record, 88(s1):2-9, 2012.
    • (2012) Economic Record , vol.88 , Issue.1 , pp. 2-9
    • Choi, H.1    Varian, H.2
  • 10
    • 42549170653 scopus 로고    scopus 로고
    • A holistic lexicon based approach to opinion mining
    • X. Ding, B. Liu, and P. S. Yu. A Holistic Lexicon Based Approach to Opinion Mining. WSDM, 2008.
    • (2008) WSDM
    • Ding, X.1    Liu, B.2    Yu, P.S.3
  • 11
    • 82555183093 scopus 로고    scopus 로고
    • Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy
    • G. Dror, N. Koenigstein, and Y. Koren. Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy. RecSys, 2011.
    • (2011) RecSys
    • Dror, G.1    Koenigstein, N.2    Koren, Y.3
  • 13
    • 78650136019 scopus 로고    scopus 로고
    • Factorization models for context-/time-Aware movie recommendations
    • Z. Gantner, S. Rendle, and L. Schmidt-Thieme. Factorization Models for Context-/Time-Aware Movie Recommendations. CAMRa, pages 14-19, 2010.
    • (2010) CAMRa , pp. 14-19
    • Gantner, Z.1    Rendle, S.2    Schmidt-Thieme, L.3
  • 15
    • 78649941975 scopus 로고    scopus 로고
    • Multiverse recommendation: N-dimensional tensor factorization for context-Aware collaborative filtering
    • A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse Recommendation: N-dimensional Tensor Factorization for Context-Aware Collaborative Filtering. RecSys, pages 79-86, 2010.
    • (2010) RecSys , pp. 79-86
    • Karatzoglou, A.1    Amatriain, X.2    Baltrunas, L.3    Oliver, N.4
  • 16
    • 70350647708 scopus 로고    scopus 로고
    • Collaborative filtering with temporal dynamics
    • Y. Koren. Collaborative Filtering with Temporal Dynamics. KDD, pages 447-455, 2009.
    • (2009) KDD , pp. 447-455
    • Koren, Y.1
  • 17
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for non-negative matrix factorization
    • D. D. Lee and H. S. Seung. Algorithms for Non-negative Matrix Factorization. NIPS, 2001.
    • (2001) NIPS
    • Lee, D.D.1    Seung, H.S.2
  • 19
    • 72249115124 scopus 로고    scopus 로고
    • A spatio temporal approach to collaborative filtering
    • Z. Lu, D. Agarwal, and I. Dhillon. A Spatio Temporal Approach to Collaborative Filtering. RecSys, 2009.
    • (2009) RecSys
    • Lu, Z.1    Agarwal, D.2    Dhillon, I.3
  • 20
    • 70349129527 scopus 로고    scopus 로고
    • A recommendation method considering users' time series contexts
    • K. Oku, S. Nakajima, J. Miyazaki, S. Uemura, and H. Kato. A Recommendation Method Considering Users' Time Series Contexts. ICUIMC, 2009.
    • (2009) ICUIMC
    • Oku, K.1    Nakajima, S.2    Miyazaki, J.3    Uemura, S.4    Kato, H.5
  • 21
  • 22
    • 80053270803 scopus 로고    scopus 로고
    • Extracting product features and opinions from reviews
    • A. M. Popescu and O. Etzioni. Extracting Product Features and Opinions from Reviews. EMNLP, 2005.
    • (2005) EMNLP
    • Popescu, A.M.1    Etzioni, O.2
  • 25
    • 78650152391 scopus 로고    scopus 로고
    • Mining mood-specific movie similarity with matrix factorization for context-Aware recommendation
    • Y. Shi, M. Larson, and A. Hanjalic. Mining Mood-specific Movie Similarity with Matrix Factorization for Context-Aware Recommendation. CAMRa, pages 34-40, 2010.
    • (2010) CAMR , pp. 34-40
    • Shi, Y.1    Larson, M.2    Hanjalic, A.3
  • 28
    • 62449225003 scopus 로고    scopus 로고
    • Investigation of various matrix factorization methods for large recommender systems
    • G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. Investigation of Various Matrix Factorization Methods for Large Recommender Systems. Proc. ICDM, 2008.
    • (2008) Proc. ICDM
    • Takacs, G.1    Pilaszy, I.2    Nemeth, B.3    Tikk, D.4
  • 29
    • 84959882687 scopus 로고    scopus 로고
    • Building large-scale twitter-specific sentiment lexicon: A representation learning approach
    • D. Tang, F. Wei, B. Qin, M. Zhou, and T. Liu. Building Large-Scale Twitter-Specific Sentiment Lexicon: A Representation Learning Approach. COLING, pages 172-182, 2014.
    • (2014) COLING , pp. 172-182
    • Tang, D.1    Wei, F.2    Qin, B.3    Zhou, M.4    Liu, T.5
  • 32
    • 84883126835 scopus 로고    scopus 로고
    • Opportunity models for e-commerce recommendation: Right product right time
    • J. Wang and Y. Zhang. Opportunity Models for E-commerce Recommendation: Right Product, Right Time. SIGIR, pages 303-312, 2013.
    • (2013) SIGIR , pp. 303-312
    • Wang, J.1    Zhang, Y.2
  • 33
    • 84928750016 scopus 로고    scopus 로고
    • Flame: A probabilistic model combining aspect based opinion mining and collaborative filtering
    • Y. Wu and M. Ester. FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering. WSDM, pages 199-208, 2015.
    • (2015) WSDM , pp. 199-208
    • Wu, Y.1    Ester, M.2
  • 34
    • 77956220287 scopus 로고    scopus 로고
    • Temporal recommendation on graphs via long-And short-Term preference fusion
    • L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal Recommendation on Graphs via Long-And Short-Term Preference Fusion. KDD, pages 723-731, 2010.
    • (2010) KDD , pp. 723-731
    • Xiang, L.1    Yuan, Q.2    Zhao, S.3    Chen, L.4    Zhang, X.5    Yang, Q.6    Sun, J.7
  • 35
    • 84868643449 scopus 로고    scopus 로고
    • Predicting epidemic tendency through search behavior analysis
    • D. Xu, Y. Liu, M. Zhang, S. Ma, A. Cui, and L. Ru. Predicting Epidemic Tendency through Search Behavior Analysis. IJCAI, pages 2361-2366, 2011.
    • (2011) IJCAI , pp. 2361-2366
    • Xu, D.1    Liu, Y.2    Zhang, M.3    Ma, S.4    Cui, A.5    Ru, L.6
  • 36
    • 80053250358 scopus 로고    scopus 로고
    • Multi-level structured models for document-level sentiment classification
    • A. Yessenalina, Y. Yue, et al. Multi-level structured models for document-level sentiment classification. EMNLP, pages 1046-1056, 2010.
    • (2010) EMNLP , pp. 1046-1056
    • Yessenalina, A.1    Yue, Y.2
  • 37
    • 84883083735 scopus 로고    scopus 로고
    • Time-aware point-of-interest recommendation
    • Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann. Time-Aware Point-of-interest Recommendation. SIGIR, pages 363-372, 2013.
    • (2013) SIGIR , pp. 363-372
    • Yuan, Q.1    Cong, G.2    Ma, Z.3    Sun, A.4    Thalmann, N.M.5
  • 38
    • 84904544672 scopus 로고    scopus 로고
    • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
    • Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis. SIGIR, pages 83-92, 2014.
    • (2014) SIGIR , pp. 83-92
    • Zhang, Y.1    Lai, G.2    Zhang, M.3    Zhang, Y.4    Liu, Y.5    Ma, S.6
  • 39
    • 84904573681 scopus 로고    scopus 로고
    • Do users rate or review? Boost phrase-level sentiment labeling with review-level sentiment classification
    • Y. Zhang, H. Zhang, M. Zhang, Y. Liu, et al. Do Users Rate or Review? Boost Phrase-level Sentiment Labeling with Review-level Sentiment Classification. SIGIR, pages 1027-1030, 2014.
    • (2014) SIGIR , pp. 1027-1030
    • Zhang, Y.1    Zhang, H.2    Zhang, M.3    Liu, Y.4
  • 40
    • 84937572394 scopus 로고    scopus 로고
    • Understanding the sparsity: Augmented matrix factorization with sampled constraints on unobservables
    • Y. Zhang, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Understanding the Sparsity: Augmented Matrix Factorization with Sampled Constraints on Unobservables. CIKM, 2014.
    • (2014) CIKM
    • Zhang, Y.1    Zhang, M.2    Zhang, Y.3    Liu, Y.4    Ma, S.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.