[1]LEI Zhen,WEN Yimin,WANG Zhiqiang,et al.Heat conduction controlled by the influence of users and items[J].CAAI Transactions on Intelligent Systems,2016,11(3):328-335.[doi:10.11992/tis.201603042]
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Heat conduction controlled by the influence of users and items
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
11
Number of periods:
2016 3
Page number:
328-335
Column:
学术论文—自然语言处理与理解
Public date:
2016-06-25
- Title:
- Heat conduction controlled by the influence of users and items
- Keywords:
- heat conduction; personalized recommendation; user’s preference; sentiment polarity; bipartite network; information overload; item popularity; user’s influence
- CLC:
- TP391
- DOI:
- 10.11992/tis.201603042
- Abstract:
- The overload of information on the Internet can lead to users feeling hopeless about finding the information they are seeking. Making accurate recommendations to users about the information they truly need is an urgent problem that must be addressed. The heat conduction (HC) algorithm has recently been applied in personalized recommendation technology, but its mechanism weakens the heat generated from the larger-degree itemsliked by the larger-degree users. To solve this problem, we propose an improved HC algorithm that is based on user influence control (THC). THC introduces two tunable parameters to better control the influence of larger-degree users’ preferences for larger-degree items on target users. We also consider a user’s comment scores and the sentiment polarity of a comment in a given scenario to accurately judge whether the user truly likes the given scenario. We also propose a new index, called a buir, which measures the ratio of the larger-degree items that are liked by larger-degree users on the recommendation list. Experimental results show that appropriately promoting the influence of larger-degree items that are liked by larger-degree users helps in making recommendations to target users regarding items in which they are truly interested, thereby improving the performance of the recommendation.
- References:
-
[1] 文益民, 史一帆, 蔡国永, 等. 个性化旅游推荐研究综述[EB/OL]. 北京: 中国科技论文在线, 2014. [2014-07-03]. http://www.paper.edu.cn/releasepaper/content/201407-56.
[2] RESNICK P, VARIAN H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3): 56-58.
[3] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE transactions on knowledge and data engineering, 2005, 17(6): 734-749.
[4] FELFERNIG A, GORDEA S, JANNACH D, et al. A short survey of recommendation technologies in travel and tourism[J]. OEGAI journal, 2007, 25(7): 17-22.
[5] LIU Jianguo, ZHOU Tao, GUO Qiang. Information filtering via biased heat conduction[J]. Physical review E, 2011, 84(3): 037101.
[6] LINDEN G, SMITH B, YORK J. Amazon. com recommendations: item-to-item collaborative filtering[J]. IEEE internet computing, 2003, 7(1): 76-80.
[7] DAS A S, DATAR M, GARG A, et al. Google news personalization: scalable online collaborative filtering[C]//Proceedings of the 16th International Conference on World wide Web. New York, USA, 2007: 271-280.
[8] LIU Qiwen, CHEN Tianjian, CAI Jing, et al. Enlister: baidu’s recommender system for the biggest chinese Q & A website[C]//Proceedings of the Sixth ACM Conference on Recommender Systems. New York, USA, 2012: 285-288.
[9] HERLOCKER J L, KONSTAN J A, RIEDL J. Explaining collaborative filtering recommendations[C]//Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work. New York, USA, 2000: 241-250.
[10] PAZZANI M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial intelligence review, 1999, 13(5-6): 393-408.
[11] ZHOU Tao, Lü Linyuan, ZHANG Yicheng. Predicting missing links via local information[J]. The european physical journal B, 2009, 71(4): 623-630.
[12] Lü Linyuan, ZHOU Tao. Link prediction in weighted networks: the role of weak ties[J]. EOL (europhysics letters), 2010, 89(1): 18001.
[13] ZHOU Tao, KUSCSIK Z, LIU Jianguo, et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J]. Proceedings of the national academy of sciences of the United States of America, 2010, 107(10): 4511-4515.
[14] ZENG Wei, SHANG Mingsheng, ZHANG Qianming, et al. Can dissimilar users contribute to accuracy and diversity of personalized recommendation[J]. International journal of modern physics C, 2010, 21(10): 1217-1227.
[15] ZHANG Zike, YU Lu, FANG Kuan, et al. Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering[J]. Physica A: statistical mechanics and its applications, 2014, 399: 82-88.
[16] ZHOU Tao, REN Jie, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Physical review E, 2007, 76(4): 046115.
[17] NIE Dacheng, AN Yahui, DONG Qiang, et al. Information filtering via balanced diffusion on bipartite networks[J]. Physica A: statistical mechanics and its applications, 2015, 421: 44-53.
[18] 侯磊, 胡兆龙, 张博, 等. 基于流行度的非平衡热传导推荐算法研究[J]. 计算机应用研究, 2015, 32(11): 3235-3237. HOU Lei, HU Zhaolong, ZHANG Bo, et al. Information filtering via non-equilibrium heat conduction with consideration of popularity[J]. Application research of computers, 2015, 32(11): 3235-3237.
[19] LIU Jianguo, GUO Qiang, ZHANG Yicheng. Information filtering via weighted heat conduction algorithm[J]. Physica A: statistical mechanics and its applications, 2011, 390(12): 2414-2420.
[20] SHI Shaoliang, LI Yunpeng, WEN Yimin, et al. Adding the sentiment attribute of nodes to improve link prediction in social network[C]//Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Zhangjiajie, China, 2015: 1263-1269.
[20] LIU Jinhu, ZHANG Zike, CHEN Lingjiao, et al. Gravity effects on information filtering and network evolving[J]. PLoS one, 2014, 9(3): e91070.
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