一種基于社交網絡社區(qū)的組推薦框架

劉宇; 吳斌; 曾雪琳; 張云雷; 王柏

doi:10.11999/JEIT160544

一種基于社交網絡社區(qū)的組推薦框架

doi: 10.11999/JEIT160544 cstr: 32379.14.JEIT160544

基金項目:

國家重點基礎研究發(fā)展計劃(2013CB329606)，北京市共建項目專項

計量
- 文章訪問數: 1604
- HTML全文瀏覽量: 205
- PDF下載量: 576
- 被引次數: 0
出版歷程
- 收稿日期: 2016-05-27
- 修回日期: 2016-07-18
- 刊出日期: 2016-09-19

A Group Recommendation Framework Based on Social Network Community

Funds:

The National Key Basic Research and Department Program of China (2013CB329606), Special Fund for Beijing Common Construction Project

摘要

摘要: 面向用戶群組的推薦主要面臨如何有意義地對群組進行定義并識別，以及向群組內用戶進行有效推薦兩大問題。該文針對已有研究在用戶群組定義解釋性不強等存在的問題，提出一種基于社交網絡社區(qū)的組推薦框架。該框架利用社交網絡結構信息發(fā)現重疊網絡社區(qū)結構作為用戶群組，具有較強的可解釋性，并根據用戶與群組間的隸屬度制定了考慮用戶對群組貢獻與用戶從群組獲利的4種聚合與分配策略，以完成組推薦任務。通過在公開數據集上與已有方法的對比實驗，驗證了該框架在組推薦方面的有效性和準確性。
- 社交網絡 /
- 組推薦 /
- 重疊網絡社區(qū) /
- 非負矩陣分解
Abstract: Group recommendation confronts two major problems, i.e., unambiguous definition and identification of groups and efficient recommendation to users in groups. To tackle the two problems, a group recommendation framework based on social network community is proposed. The framework takes into account social network structural information to identify overlapping groups, which is well interpreted; and fulfills the task of recommending to groups by performing aggregation and allocation strategies using the membership of users related to groups, which considers how much users contribute to groups and benefit from groups. Experimental results on publicly open datasets demonstrate its efficiency and accuracy on the task of group recommendation.
- Social network /
- Group recommendation /
- Overlapping network community /
- Non-negative matrix factorization

HTML全文

參考文獻(27)

MASTHOFF J. Recommender Systems Handbook[M]. Boston, MA: Springer US, ch. Group Recommender Systems: Combining Individual Models, 2011: 677-702.

JAMESON A and SMYTH B. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin, Heidelberg: Springer Berlin Heidelberg, ch. Recommendation to Groups, 2007: 596-627.

AMER-YAHIA S, ROY S, CHAWLAT A, et al. Group recommendation: Semantics and efficiency[J]. Proceedings of the VLDB Endowment, 2009, 2(1): 754-765. doi: 10.14778/ 1687627.1687713.

OCONNOR M, COSLEY D, KONSTAN J, et al. ECSCW 2001: Proceedings of the Seventh European Conference on Computer Supported Cooperative Work 1620 September 2001[M]. Bonn, Germany. Dordrecht: Springer Netherlands, ch. PolyLens: A Recommender System for Groups of Users, 2001: 199-218.

DE CAMPOS L M, FERNANDEZ-LUNA J M, HUETE J F, et al. Group recommending: a methodological approach based on bayesian networks[C]. IEEE 23rd International Conference on Data Engineering Workshop, Istanbul, Turkey, 2007: 835-844.

OHARA K, LIPSON M, JANSEN M, et al. Jukola: democratic music choice in a public space[C]. Proceedings of the 5th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, New York, USA, 2004: 145-154.

SPRAGUE D, WU F, and TORY M. Music selection using the partyvote democratic jukebox[C]. Proceedings of the Working Conference on Advanced Visual Interfaces, New York, USA, 2008: 433-436.

CHAO D L, BALTHROP J, and FORREST S. Adaptive radio: achieving consensus using negative preferences[C]. Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work, New York, USA, 2005: 120-123.

MCCARTHY J F and ANAGNOST T D. Musicfx: An arbiter of group preferences for computer supported collaborative workouts[C]. Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, New York, USA, 1998: 363-372.

SHI J, WU B, and LIN X. A latent group model for group recommendation[C]. 2015 IEEE International Conference on Mobile Services, New York, USA, 2015: 233-238.

BALTRUNAS L, MAKCINSKAS T, and RICCI F. Group recommendations with rank aggregation and collaborative filtering[C]. Proceedings of the Fourth ACM Conference on Recommender Systems, New York, USA, 2010: 119-126.

PAZZANI M J and BILLSUS D. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin, Heidelberg: Springer Berlin Heidelberg, ch. Content-Based Recommendation Systems, 2007: 325-341.

SHI Y, LARSON M, and HANJALIC A. Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges[J]. ACM Computing Surveys, 2014, 47(1): 1-45. doi: 10.1145/2556270.

王玉斌, 孟祥武, 胡勛. 一種基于信息老化的協同過濾推薦算法[J]. 電子與信息學報, 2013, 35(10): 2391-2396. doi: 10.3724 /SP.J.1146.2012.01743.

WANG Y, MENG X, and HU X. Information aging-based collaborative filtering recommendation algorithm[J]. Journal of Electronics Information Technology, 2013, 35(10): 2391-2396. doi: 10.3724/SP.J.1146.2012.01743.

邢星. 社交網絡個性化推薦方法研究[D]. [博士論文], 大連海事大學, 2013.

XING X. Research on recommendation methods in social networks[D]. [Ph.D. dissertation], Dalian Maritime University, 2013.

涂丹丹, 舒承椿, 余海燕. 基于聯合概率矩陣分解的上下文廣告推薦算法[J]. 軟件學報, 2013, 24(3): 454-464. doi: 10.3724/ SP.J.1001.2013.04238.

TU D, SHU C, and YU H. Using unified probabilistic matrix factorization for contextual advertisement recommendation [J]. Journal of Software, 2013, 24(3): 454-464. doi: 10.3724/ SP.J.1001.2013.04238.

GIRVAN M and NEWMAN M E. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences, 2002, 99(12): 7821-7826. doi: 10.1073/ pnas.122653799.

BORATTO L and CARTA S. Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system[C]. Proceedings of 16th International Conference on Enterprise Information Systems, Lisbon, Portugal, 2014: 564-572.

方耀寧, 郭云飛, 丁雪濤, 等. 一種基于局部結構的改進奇異值分解推薦算法[J]. 電子與信息學報, 2013, 35(6): 1284-1289. doi: 10.3724/SP.J.1146.2012.01299.

FANG Y, GUO Y, DING X, et al. An improved singular value decomposition recommender algorithm based on local structures[J]. Journal of Electronics Information Technology, 2013, 35(6): 1284-1289. doi: 10.3724/SP.J.1146. 2012.01299.

DING C, LI T, and PENG W, et al. Orthogonal nonnegative matrix t-factorizations for clustering[C]. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2006: 126-135.

CANTADOR I, and CASTELLS P. Extracting multilayered communities of interest from semantic user profiles: application to group modeling and hybrid recommendations [J]. Computers in Human Behavior, 2011, 27(4): 1321-1336. doi: 10.1016/j.chb.2010.07.027.

SHI X, LU H, HE Y, et al. Community detection in social network with pairwisely constrained symmetric non-negative matrix factorization[C]. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, 2015: 541-546.

PSORAKIS I, ROBERTS S, EDBEN M, et al. Overlapping community detection using Bayesian non-negative matrix factorization[J]. Physical Review E, 2011, 83(6): 066114. doi: 10.1103/PhysRevE.83.066114.

施引文獻

資源附件(0)

訪問統(tǒng)計