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Volumn 19-23-Oct-2015, Issue , 2015, Pages 1661-1670

TriRank: Review-aware explainable recommendation by modeling aspects

Author keywords

Aspects; Explanable recommendation; Reviews; Top N recommendation; Tripartite graph ranking

Indexed keywords

BINS; COLLABORATIVE FILTERING; COMPUTER PROGRAMMING; REVIEWS;

EID: 84959275581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2806416.2806504     Document Type: Conference Paper
Times cited : (547)

References (36)
  • 1
    • 57349151435 scopus 로고    scopus 로고
    • Video suggestion and discovery for youtube: Taking random walks through the view graph
    • S. Baluja, R. Seth, and D. Sivakumar. Video suggestion and discovery for Youtube: Taking random walks through the view graph. In Proc. of WWW '08, pages 895-904, 2008.
    • (2008) Proc. of WWW '08 , pp. 895-904
    • Baluja, S.1    Seth, R.2    Sivakumar, D.3
  • 3
    • 78649914423 scopus 로고    scopus 로고
    • Performance of recommender algorithms on top-n recommendation tasks
    • P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proc. of RecSys '10, pages 39-46, 2010.
    • (2010) Proc. of RecSys '10 , pp. 39-46
    • Cremonesi, P.1    Koren, Y.2    Turrin, R.3
  • 4
    • 84907027192 scopus 로고    scopus 로고
    • Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars)
    • Q. Diao, M. Qiu, C.-Y. Wu, A. J. Smola, J. Jiang, and C. Wang. Jointly modeling aspects, ratings and sentiments for movie recommendation (jmars). In Proc. of KDD '14, pages 193-202, 2014.
    • (2014) Proc. of KDD '14 , pp. 193-202
    • Diao, Q.1    Qiu, M.2    Wu, C.-Y.3    Smola, A.J.4    Jiang, J.5    Wang, C.6
  • 5
    • 79957953803 scopus 로고    scopus 로고
    • Beyond the stars: Improving rating predictions using review text content
    • G. Ganu, N. Elhadad, and A. Marian. Beyond the stars: Improving rating predictions using review text content. In Proc. of WebDB '09, 2009.
    • (2009) Proc. of WebDB '09
    • Ganu, G.1    Elhadad, N.2    Marian, A.3
  • 6
    • 84880877231 scopus 로고    scopus 로고
    • Itemrank: A random-walk based scoring algorithm for recommender engines
    • M. Gori and A. Pucci. Itemrank: A random-walk based scoring algorithm for recommender engines. In Proc. of IJCAI '07, pages 2766-2771, 2007.
    • (2007) Proc. of IJCAI '07 , pp. 2766-2771
    • Gori, M.1    Pucci, A.2
  • 8
    • 77953061730 scopus 로고    scopus 로고
    • Topic-sensitive PageRank
    • T. H. Haveliwala. Topic-sensitive PageRank. In Proc. of WWW '02, pages 517-526, 2002.
    • (2002) Proc. of WWW '02 , pp. 517-526
    • Haveliwala, T.H.1
  • 9
    • 84904580359 scopus 로고    scopus 로고
    • Predicting the popularity of web 2.0 items based on user comments
    • X. He, M. Gao, M.-Y. Kan, Y. Liu, and K. Sugiyama. Predicting the popularity of web 2.0 items based on user comments. In Proc. SIGIR '14, pages 233-242, 2014.
    • (2014) Proc. SIGIR '14 , pp. 233-242
    • He, X.1    Gao, M.2    Kan, M.-Y.3    Liu, Y.4    Sugiyama, K.5
  • 10
    • 84904569042 scopus 로고    scopus 로고
    • Comment-based multi-view clustering of web 2.0 items
    • X. He, M.-Y. Kan, P. Xie, and X. Chen. Comment-based multi-view clustering of web 2.0 items. In Proc. of WWW '14, pages 771-782, 2014.
    • (2014) Proc. of WWW '14 , pp. 771-782
    • He, X.1    Kan, M.-Y.2    Xie, P.3    Chen, X.4
  • 11
    • 84904559853 scopus 로고    scopus 로고
    • Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction
    • L. Hu, A. Sun, and Y. Liu. Your neighbors affect your ratings: On geographical neighborhood influence to rating prediction. In Proc. of SIGIR '14, pages 345-354, 2014.
    • (2014) Proc. of SIGIR '14 , pp. 345-354
    • Hu, L.1    Sun, A.2    Liu, Y.3
  • 12
    • 12244305149 scopus 로고    scopus 로고
    • Mining and summarizing customer reviews
    • M. Hu and B. Liu. Mining and summarizing customer reviews. In Proc. of KDD '04, pages 168-177, 2004.
    • (2004) Proc. of KDD '04 , pp. 168-177
    • Hu, M.1    Liu, B.2
  • 13
    • 71149114015 scopus 로고    scopus 로고
    • A novel lexicalized hmm-based learning framework for web opinion mining
    • W. Jin and H. H. Ho. A novel lexicalized hmm-based learning framework for web opinion mining. In Proc. of ICML '09, pages 465-472, 2009.
    • (2009) Proc. of ICML '09 , pp. 465-472
    • Jin, W.1    Ho, H.H.2
  • 14
    • 80052883059 scopus 로고    scopus 로고
    • Advances in collaborative filtering
    • Springer US
    • Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 145-186. Springer US, 2011.
    • (2011) Recommender Systems Handbook , pp. 145-186
    • Koren, Y.1    Bell, R.2
  • 15
    • 82555195666 scopus 로고    scopus 로고
    • Random walk based entity ranking on graph for multidimensional recommendation
    • S. Lee, S.-i. Song, M. Kahng, D. Lee, and S.-g. Lee. Random walk based entity ranking on graph for multidimensional recommendation. In Proc. of RecSys '11, pages 93-100, 2011.
    • (2011) Proc. of RecSys '11 , pp. 93-100
    • Lee, S.1    Song, S.-I.2    Kahng, M.3    Lee, D.4    Lee, S.-G.5
  • 16
    • 0037252945 scopus 로고    scopus 로고
    • Amazon.com recommendations: Item-to-item collaborative filtering
    • IEEE Jan
    • G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76-80, Jan 2003.
    • (2003) Internet Computing , vol.7 , Issue.1 , pp. 76-80
    • Linden, G.1    Smith, B.2    York, J.3
  • 17
    • 84908884026 scopus 로고    scopus 로고
    • Ratings meet reviews, a combined approach to recommend
    • G. Ling, M. R. Lyu, and I. King. Ratings meet reviews, a combined approach to recommend. In Proc. of RecSys '14, pages 105-112, 2014.
    • (2014) Proc. of RecSys '14 , pp. 105-112
    • Ling, G.1    Lyu, M.R.2    King, I.3
  • 18
    • 57349097660 scopus 로고    scopus 로고
    • Eigenrank: A ranking-oriented approach to collaborative filtering
    • N. N. Liu and Q. Yang. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proc. of SIGIR '08, pages 83-90, 2008.
    • (2008) Proc. of SIGIR '08 , pp. 83-90
    • Liu, N.N.1    Yang, Q.2
  • 19
    • 84887587513 scopus 로고    scopus 로고
    • Hidden factors and hidden topics: Understanding rating dimensions with review text
    • J. McAuley and J. Leskovec. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proc. of RecSys'13, pages 165-172, 2013.
    • (2013) Proc. of RecSys'13 , pp. 165-172
    • McAuley, J.1    Leskovec, J.2
  • 21
    • 84883115169 scopus 로고    scopus 로고
    • Sentiment analysis of user comments for one-class collaborative filtering over ted talks
    • N. Pappas and A. Popescu-Belis. Sentiment analysis of user comments for one-class collaborative filtering over ted talks. In Proc. of SIGIR '13, pages 773-776, 2013.
    • (2013) Proc. of SIGIR '13 , pp. 773-776
    • Pappas, N.1    Popescu-Belis, A.2
  • 22
    • 84884477955 scopus 로고    scopus 로고
    • Opinion-driven matrix factorization for rating prediction
    • Š. Pero and T. Horváth. Opinion-driven matrix factorization for rating prediction. In Proc. of UMAP '13, pages 1-13. 2013.
    • (2013) Proc. of UMAP '13 , pp. 1-13
    • Pero, S.1    Horváth, T.2
  • 24
    • 85052617391 scopus 로고    scopus 로고
    • Item-based collaborative filtering recommendation algorithms
    • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW '01, pages 285-295, 2001.
    • (2001) Proc. of WWW '01 , pp. 285-295
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.4
  • 26
    • 84927539312 scopus 로고    scopus 로고
    • Text-based user-knn: Measuring user similarity based on text reviews
    • M. Terzi, M. Rowe, M.-A. Ferrario, and J. Whittle. Text-based user-knn: Measuring user similarity based on text reviews. In Proc. of UMAP '14, pages 195-206. 2014.
    • (2014) Proc. of UMAP '14 , pp. 195-206
    • Terzi, M.1    Rowe, M.2    Ferrario, M.-A.3    Whittle, J.4
  • 27
    • 79960495877 scopus 로고    scopus 로고
    • Designing and evaluating explanations for recommender systems
    • Springer US
    • N. Tintarev and J. Masthoff. Designing and evaluating explanations for recommender systems. In Recommender Systems Handbook, pages 479-510. Springer US, 2011.
    • (2011) Recommender Systems Handbook , pp. 479-510
    • Tintarev, N.1    Masthoff, J.2
  • 28
    • 72249096877 scopus 로고    scopus 로고
    • Tagsplanations: Explaining recommendations using tags
    • J. Vig, S. Sen, and J. Riedl. Tagsplanations: Explaining recommendations using tags. In Proc. of IUI '09, pages 47-56, 2009.
    • (2009) Proc. of IUI '09 , pp. 47-56
    • Vig, J.1    Sen, S.2    Riedl, J.3
  • 29
    • 77956220287 scopus 로고    scopus 로고
    • Temporal recommendation on graphs via longand short-term preference fusion
    • L. Xiang, Q. Yuan, S. Zhao, L. Chen, X. Zhang, Q. Yang, and J. Sun. Temporal recommendation on graphs via longand short-term preference fusion. In Proc. of KDD '10, pages 723-732, 2010.
    • (2010) Proc. of KDD '10 , pp. 723-732
    • Xiang, L.1    Yuan, Q.2    Zhao, S.3    Chen, L.4    Zhang, X.5    Yang, Q.6    Sun, J.7
  • 30
    • 84937563514 scopus 로고    scopus 로고
    • Collaborative filtering incorporating review text and co-clusters of hidden user communities and item groups
    • Y. Xu, W. Lam, and T. Lin. Collaborative filtering incorporating review text and co-clusters of hidden user communities and item groups. In Proc. of CIKM '14, pages 251-260, 2014.
    • (2014) Proc. of CIKM '14 , pp. 251-260
    • Xu, Y.1    Lam, W.2    Lin, T.3
  • 31
  • 32
    • 79955704875 scopus 로고    scopus 로고
    • Extracting and ranking product features in opinion documents
    • L. Zhang, B. Liu, S. H. Lim, and E. O'Brien. Extracting and ranking product features in opinion documents. In Proc. of COLING '10, pages 1462-1470, 2010.
    • (2010) Proc. of COLING '10 , pp. 1462-1470
    • Zhang, L.1    Liu, B.2    Lim, S.H.3    O'Brien, E.4
  • 33
    • 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, and S. Ma. Do users rate or review?: Boost phrase-level sentiment labeling with review-level sentiment classification. In Proc. of SIGIR '14, pages 1027-1030, 2014.
    • (2014) Proc. of SIGIR '14 , pp. 1027-1030
    • Zhang, Y.1    Zhang, H.2    Zhang, M.3    Liu, Y.4    Ma, S.5
  • 34
    • 84904544672 scopus 로고    scopus 로고
    • Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
    • Y. Zhang, M. Zhang, Y. Zhang, Y. Liu, and S. Ma. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proc. of SIGIR '14, pages 83-92, 2014.
    • (2014) Proc. of SIGIR '14 , pp. 83-92
    • Zhang, Y.1    Zhang, M.2    Zhang, Y.3    Liu, Y.4    Ma, S.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.