Tensor-based tag emotion aware recommendation with probabilistic ranking = Tensor-based tag emotion aware recommendation with probabilistic ranking

다국어 초록 (Multilingual Abstract)

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

참고문헌 (Reference)

1 M. Ames, "Why we tag : motivations for annotation in mobile and online media" 971-980, 2007

2 O. Nov, "Why do people tag? : motivations for photo tagging" 53 (53): 128-131, 2010

3 Z. Li, "Weakly supervised deep matrix factorization for social image understanding" 26 (26): 276-288, 2017

4 C. L. Huang, "Utilizing user tag-based interests in recommender systems for social resource sharing websites" 56 : 86-96, 2014

5 D. Rafailidis, "The TFC model : Tensor factorization and tag clustering for item recommendation in social tagging systems" 43 (43): 673-688, 2013

6 N. Ifada, "Tensor-based Item Recommendation using Probabilistic Ranking in Social Tagging Systems" 805-810, 2014

7 S. Sen, "Tagging, communities, vocabulary, evolution" 181-190, 2006

8 K. H. L. Tso-Sutter, "Tag-aware recommender systems by fusion of collaborative filtering algorithms" 1995-1999, 2008

9 P. Symeonidis, "Tag recommendations based on tensor dimensionality reduction" 43-50, 2008

10 A. K. Milicevic, "Social tagging in recommender systems : A survey of the state-of-the-art and possible extensions" 33 (33): 187-209, 2010

11 I. Guy, "Social media recommendation based on people and tags" 194-201, 2010

12 L. F. Spiteri, "Social cataloguing sites : features and implications for cataloguing practique and the public library catalogue" 769-785, 2009

13 E. Cambria, "SenticNet: A Publicly Available Semantic Resource for Opinion Mining" 10 (10): 2010

14 Z. Li, "Robust structured nonnegative matrix factorization for image representation" 29 (29): 1947-1960, 2018

15 J. Bobadilla, "Recommender systems survey" 46 : 109-132, 2013

16 F. O. Isinkaye, "Recommendation systems : Principles, methods and evaluation" 16 (16): 261-273, 2015

17 R. Katarya, "Recent developments in affective recommender systems" 461 : 182-190, 2016

18 F. Gedikli, "Rating items by rating tags" 25-32, 2010

19 M. Á. García-Cumbreras, "Pessimists and optimists : Improving collaborative filtering through sentiment analysis" 40 (40): 6758-6765, 2013

20 J. Sun, "Mining affective text to improve social media item recommendation" 51 (51): 444-457, 2015

21 Y. Koren, "Matrix factorization techniques for recommender systems" 42 (42): 2009

22 D. Vallet, "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)" 420-431, 2010

23 J. Buder, "Learning with personalized recommender systems : A psychological view" 28 (28): 207-216, 2012

24 B. Sarwar, "Item-based collaborative filtering recommendation algorithms" 285-295, 2001

25 H. Lim, "Item recommendation using tag emotion in social cataloging services" 89 : 179-187, 2017

26 A. Nanopoulos, "Item recommendation in collaborative tagging systems" 41 (41): 760-771, 2011

27 H. Xie, "Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy" 52 (52): 61-72, 2016

28 Z. Qingbiao, "Incorporating sentiment analysis for improved tag-based recommendation" 1222-1227, 2011

29 H. Kim, "Improving recommendation based on implicit trust relationships from tags" 25-30, 2012

30 F. Gedikli, "Improving recommendation accuracy based on item-specific tag preferences" 4 (4): 11-, 2013

31 Z.-K. Zhang, "Hybrid recommendation algorithm based on two roles of social tags" 22 (22): 1250166-, 2012

32 V. Batagelj, "Generalized cores"

33 S. Xu, "Exploring folksonomy for personalized search" 155-162, 2008

34 G. Gonzalez, "Embedding emotional context in recommender systems" 845-852, 2007

35 Z. Li, "Deep Collaborative Embedding for Social Image Understanding" 41 (41): 2019

36 Y. Xu, "Cubic analysis of social bookmarking for personalized recommendation" 733-738, 2006

37 R. Dong, "Combining similarity and sentiment in opinion mining for product recommendation" 46 (46): 285-312, 2016

38 H. -N. Kim, "Collaborative user modeling with user-generated tags for social recommender systems" 38 (38): 8488-8496, 2011

39 J. Peng, "Collaborative filtering in social tagging systems based on joint item-tag recommendations" 809-818, 2010

40 P. de Meo, "Analyzing user behavior across social sharing environments" 5 (5): 14-, 2013

41 M. Tkalčič, "Affective recommender systems: the role of emotions in recommender systems" 9-13, 2011

42 M. Zhao, "Adapting Document Ranking to Users’ Preferences Using Click-Through Data" 26-42, 2006

43 P. Symeonidis, "A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis" 22 (22): 179-192, 2010

44 L. De Lathauwer, "A multilinear singular value decomposition" 21 (21): 1253-1278, 2000

45 K. Sparck Jones, "A Probabilistic Model of Onformation Retrieval : development and comparative experiments : Part 1" 63 (63): 779-808, 2000