An Unbiased User Model for Interest Diffusion in the Heterogeneous Network Recommendation
Pages 138 - 144
Abstract
Interest diffusion in a bipartite network has been verified effective for personality recommendation. User nodes and item nodes compose a heterogeneous bipartite network. Treating user attitude toward items as interest resource and allocating it in the heterogeneous network along the linkage make it possible to recommend the most likely interested items to target users. However, the way it models user interest and quantizes interest lead some bias into the algorithm. Such as, it adopts the positive attitude into the interest diffusion process but without seeing that negative attitude also reflect user flavor and should be introduced into the computation. To overcome the drawbacks, this paper proposed an unbiased user interest model. Firstly, the unbiased model considers both positive and negative attitudes into user interest to eliminate the negative feedback discrimination. Secondly, it adopts a new criterion to divide interest from a perspective of statistic optimization. Last, it treats positive interest and negative interest equally to lower the rating bias by a symmetric formula when quantizing and weighting interest. Extensive experiments conducted on real data validate the advantage of the proposed model even with the same following diffusion process as the baseline algorithm.
References
[1]
Balabanovic Marko, Shoham Yoav. Fab: Content-based Collaborative Recommendation. COMM of the ACM, 40(3): 66--72, 1997.
[2]
Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon. GroupLens: Applying Collaborative Filtering to Usenet News. COMM of the ACM, 40(3): 77--87, 1997.
[3]
Mohammad Khoshneshin, Nick W. Street. Collaborative Filtering via Euclidean Embedding. ACM RecSys'10, September 26-30, 2010, Barcelona, Spain.
[4]
Yehuda Koren. Collaborative Filtering with Temporal Dynamics. ACM KDD'09, June 28-July 1, 2009, Paris, France.
[5]
Yehuda Koren, Robert Bell, Chris Volinsky. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 42(8): 30--37, 2009.
[6]
Yue Shi, Martha Larson, Alan Hanjalic. Mining Contextual Movie Similarity with Matrix Factorization for Context-Aware Recommendation. ACM TIST, 04 (01):1601--1619, 2013.
[7]
Hugo Liu and Pattie Maes. InterestMap: Harvesting Social Network Profiles for Recommendations. IUI'05, January 9, 2005, San Diego, California, USA.
[8]
Tao Zhou, Jie Ren, Matús Medo, Yicheng Zhang. Bipartite Network Projection and Personal Recommendation. PHYS REV E, 76(4), 046115, 2007.
[9]
Yicheng Zhang, Marcel Blattner and Yikuo Yu. Heat Conduction Process on Community Networks as a Recommendation Model. PHYS REV LETT 99, 154301, 2007.
[10]
Yicheng Zhang, Matús Medo, Jie Ren, Tao Zhou, Tao Li and Fan Yang. Recommendation Model Based on Opinion Diffusion, EUROPHYS LETT 80(2008), 68003, 2008.
[11]
Yolanda Blanco Fernandez, Jose Pazos Arias, Alberto Gil Solla, Manuel Ramos Cabrer, Martin Lopez Nores. Providing Entertainment by Content-based Filtering and Semantic Reasoning in Intelligent Recommender Systems. IEEE TCONSUM ELECTR, 54(2), 727--735, 2008.
[12]
Moshe Unger, Ariel Bar, Bracha Shapira, Lior Rokach. Towards latent context-aware recommendation systems {J}. Knowledge-Based Systems, 2016, 104.
[13]
Katzman Jared L, Shaham Uri, Cloninger Alexander, Bates Jonathan, Jiang Tingting, Kluger Yuval. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network {J}. BMC medical research methodology, 2018, 18(1).
[14]
Wenyan Song, Tomohiko Sakao. An environmentally conscious PSS recommendation method based on users' vague ratings: A rough multi-criteria approach {J}. Journal of Cleaner Production, 2018, 172.
[15]
Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl. Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems. In: Proceedings of ICCIT, April 2-4, 2002.
[16]
Fengkun Liu, Hong Joo Lee. Use of Social Network Information to Enhance Collaborative Filtering Performance. EXPERT SYST APPL, 37(7): 4772--4778, 2010.
[17]
Jianguo Liu, Tao Zhou, Hongan Che, Binghong Wang, Yicheng Zhang. Effects of High-order Correlations on Personalized Recommendations for Bipartite Networks. PHYSICA A, 389(2010), 881--886, 2010.
[18]
Jianguo Liu, Tao Zhou, Qiang Guo. Information Filtering via Biased Heat Conduction. PHYS REV E 84, 037101, 2011.
[19]
Jiani Quan, Yuchen Fu. A Novel Collaborative Filtering Algorithm Based on Bipartite Network Projection. JDCTA. 6(1): 391--397, 2012.
[20]
Sumedh Sawant. Collaborative Filtering using Weighted Bipartite Graph Projection: A Recommendation System for Yelp. CS224W: Social and Information Network Analysis, December 10, 2013.
Index Terms
- An Unbiased User Model for Interest Diffusion in the Heterogeneous Network Recommendation
Recommendations
Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge ManagementPersonalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in e-commerce. Researchers and practitioner are continuously trying to devise new methodologies to improve the accuracy of ...
Improving the Performance of Personalized Recommendation with Ontological User Interest Model
CIS '11: Proceedings of the 2011 Seventh International Conference on Computational Intelligence and SecurityPersonalized recommendation is effective to provide good recommendations to different users to meet different needs. However, it remains a challenge to make personalized recommendation sensitive to the semantic information of a user's specific context ...
User's Interests-Based Movie Recommendation in Heterogeneous Network
IIKI '15: Proceedings of the 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI)For large data set of movies and various users' interests, recommender systems should pay attention to time consumption and personal interest. However, existing information filtering techniques rarely research users' interests in movies. In order to ...
Comments
Information & Contributors
Information
Published In
July 2018
202 pages
ISBN:9781450364812
DOI:10.1145/3241748
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Beijing University of Technology
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 July 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICEBT 2018
ICEBT 2018: 2018 The 2nd International Conference on E-Education, E-Business and E-Technology
July 5 - 7, 2018
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 44Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in