The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Feature weighting in content based recommendation system using social network analysis
Abstract
We propose a hybridization of collaborative filtering and content based recommendation system. Attributes used for content based recommendations are assigned weights depending on their importance to users. The weight values are estimated from a set of linear regression equations obtained from a social network graph which captures human judgment about similarity of items.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“it is also possible to use machine learning methods that can learn from population and individual user data to automatically adapt the form of interventions to meet the user’s needs and capabilities [25-27].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [25]
Citations
Cites
Hybrid Recommender Systems: Survey and Experiments
Burke, R. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021240730564 · Cited by 4,119
Content-boosted collaborative filtering for improved recommendations
Melville, P., Mooney, R. J., & Nagarajan, R. (2002) · doi:10.5555/777092.777124 · Cited by 1,104
(2001) · Choice Reviews Online · doi:10.5860/choice.38sup-186 · Cited by 147
Cited by
Sorted by citations
Items 201–252 of 252
GeoRSA: Geospatial Recommendation System using Sentimental Analysis
Dejene, F. M., Sahoo, A. K., Barik, R. K., & Mishra, B. S. P. (2022) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc56933.2022.10053118 · Cited by 1
Movie recommendation app with cosine similarity and flask
Vedhavyas, P., Srikar, V. N. S. R., Ashwarda, N., & Begum, R. (2024) · MATEC Web of Conferences · doi:10.1051/matecconf/202439201071 · Cited by 1
A Content-Aware Expert Recommendation Scheme in Social Network Services
Shin, Y.-S., Kim, H., & Chang, J. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-1536-6_7 · Cited by 1
A Fuzzy Logic Based Recommendation System for Classified Advertisement Websites
Sharif, U., Kamal, M. R., & Rahman, R. M. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-57261-1_25 · Cited by 1
Iltanen, K., Siermala, M., Juhola, M., & Varpa, K. (2017) · International Journal of Data Science · doi:10.1504/ijds.2017.10007392 · Cited by 1
Improving Collaborative Filtering Recommendation
Chu, P.-M., Tsai, H.-R., Lee, S.-J., & Pan, S. (2018) · International Conference on Control, Automation and Artificial Intelligence (CAAI) · doi:10.2991/caai-18.2018.25 · Cited by 1
UPLBSN: User Profiling in Location-Based Social Networking
Vasanthakumar, G. U., Ashwini, G. C., Srilekha, K., Swathi, S., Acharya, A., Shenoy, P. D., & Venugopal, K. R. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1708-8_54 · Cited by 1
A Fusion Approach for Collaborative Filtering
Das, S., & Dutta, S. (2019) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3319921.3319973 · Cited by 1
Air Quality Index Forecasting using ANN and LSTM Neural Network
Gajbhiye, V. K., & Adhikari, J. (2021) · International Journal of Innovations in Engineering and Science · doi:10.46335/ijies.2020.5.12.5 · Cited by 1
Clustering approach based on feature weighting for recommendation system in movie domain
Rafsanjani, N., & Hossein, A. (2013) · Cited by 1
LOCAL APPROACHES FOR COLLABORATIVE FILTERING
Lee, J. (2015) · SMARTech Repository (Georgia Institute of Technology) · Cited by 1
What can we recommend to game players? - Implementing a system of analyzing game reviews
Huang, J. (2018) · Tampere University Institutional Repository (Tampere University) · Cited by 1
Efficient Distributed Transposition Of Large-Scale Multigraphs And High-Cardinality Sparse MatricesPreprint
Magalhães, B. R. C., & Schürmann, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06012 · Cited by 1
Dinâmica das preferências do usuário em redes sociais que evoluem ao longo do tempo
Pereira, F. S. F. (2018) · doi:10.14393/ufu.te.2018.804 · Cited by 1
Hawthorne, Mercer, F., Tzeng, D., & Hartman, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3181316/v1 · Cited by 1
Social Recommender systems: Improving recommendations through personalization
Surti, T. (2011) · Cited by 1
Exploring link, text and spatial-temporal data in social media
Yin, Z. (2012) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
On Predicting User Affiliations Using Social Features in Online Social Networks
Nguyen, M. T. (2014) · UPT. Syiah Kuala University Library (Syiah Kuala University)
A Review Paper On Exploring Text, Link And Spacial-Temporal Information In Social Media Networks
Madan, M., Chopra, M., & Nijhawan, V. K. (2015) · International journal of scientific and technology research
Method to Improve Data Sparsity Problem of Collaborative Filtering Using Latent Attribute Preference
Kwon, H.-J., & Hong, K. (2013) · Journal of Internet Computing and services · doi:10.7472/jksii.2013.14.5.59
A Personalized Recommender Algorithm Based on Semantic Tree
Xuan, Z., Xia, H., & Miao, J. (2011) · International Joint Conference on Computational Sciences and Optimization (CSO) · doi:10.1109/cso.2011.52
KEY USERS AND BOX OFFICE ANALYSIS IN AN INTEREST BASED VIRTUAL COMMUNITY
Ting, I., Wang, S.-L., & Chen, P. (2014)
An Intelligent Matching System for the Products of Small Business/Manufactures with the Celebrities
Jeong, J., Son, Y., Ko, S., & Oh, S. (2016) · MATEC Web of Conferences · doi:10.1051/matecconf/20165405005
Development of Model for Providing Feasible Scholarship
Dhika, H. (2016) · CommIT (Communication and Information Technology) Journal · doi:10.21512/commit.v10i1.1666
Understanding Your History: Multi-dimensional Data Stream Visualization of Personal Lifelogging Data
Hong, M., & Jung, J. J. (2017) · International Conference on Intelligent Environments (IE) · doi:10.1109/ie.2017.13
A unified framework for flickr group recommendation based on tetradic decomposition
Wang, X., Zhao, X., Zhou, J., & Xu, M. (2017) · International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS) · doi:10.1109/iccss.2017.8091429
Scholar Recommendation Model in Large Scale Academic Social Networking Platform
Chen, M., Li, C., Liu, J., Meng, D., & Tang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-74521-3_48
An architecture for evolving the electronic programme guide for online viewing
Mohammed, E. A. (2017) · University of Salford Institutional Repository (University of Salford)
Propagation of content similarity through a collaborative network for live show recommendationPreprint
Creusefond, J., & Matthieu Latapy, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09073
Wu, J., Zhou, B., & Shen, J. (2018) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2018.8526974
Enriching Article Recommendation with Phrase AwarenessPreprint
Chen, C., Chou, S.-C., & Ku, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01808
Markov Chain Monte Carlo for Effective Personalized Recommendations
Papilaris, M.-A., & Chalkiadakis, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-14174-5_13
Uncovering Political Promotion in China: A Network Analysis of Patronage Relationship in AutocracyPreprint
Wu, Z., Luo, J., & Zhang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00625
Attribute-based Sentiment Analysis for Recommendation
Wang, Q., & Yang, P. (2018) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2018.8781065
Pereira, F. S. F., Linhares, C. D. G., Ponciano, J. R., da Gama, J. M. P., de Amo, S., & Oliveira, G. M. B. (2019) · iSys - Brazilian Journal of Information Systems · doi:10.5753/isys.2019.599
Efficient Approximate Nearest Neighbor Search for Multiple Weighted l p≤2 Distance Functions.Preprint
Hu, H., & Li, J. (2020) · arXiv (Cornell University)
Revisiting SVD to generate powerful Node Embeddings for Recommendation SystemsPreprint
Budhiraja, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.03665
Multi-agent celebrity recommender system (MACeRS): Twitter use case
Tajbakhsh, M. S., Emamgholizadeh, H., Solouk, V., Ayough, F., & Bagherzade, J. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00845-w
(2016) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2016.06.121
DEVELOPMENT OF MODEL FOR PROVIDING FEASIBLE SCHOLARSHIPPreprint
Dhika, H. (2018) · doi:10.31227/osf.io/s7ehb
MSRHNN:Multidimensional Social Relation under Heterogeneous Neural Network for RecommendationPreprint
Millette, R., Pinard, E., Blaise, B., & Vanier, C. (2023) · Research Square · doi:10.21203/rs.3.rs-2716622/v1
Data, Recommendation Techniques, and View (DRV) Model for Online Transaction
Ali, A., Ibrahim, W., & Amin, S. Z. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35308-6_12
Transformer and Deep CNN-based Product Recommendation System
Potti, M., Chalumuri, A., Golagani, V., & Harini, D. N. D. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307936
Lopera-Mármol, M., López-González, J., & Iranzo, I. P. (2024) · Con A de animación · doi:10.4995/caa.2024.21022
Data-driven evaluation of contextual bandit algorithms and applications to dynamic recommendation
Nicol, O. (2014) · theses.fr (ABES) · doi:10.70675/05df8a22z6d06z498dza8aez024e879a9565
Engineering enhancements for movie recommender systems
Solanki, S. S. (2012) · K-State Research Exchange (Kansas State University)
Metadata
- Authors
- Debnath, S., Ganguly, N., & Mitra, P.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 1041-1042
- OpenAlex
- W2096235681
- Cited by
- 253
- References
- 3
- Citations by year
- 2025: 1 · 2024: 7 · 2023: 11 · 2022: 8 · 2021: 7 · 2020: 14 · 2019: 12 · 2018: 33 · 2017: 23 · 2016: 21 · 2015: 13 · 2014: 20 · 2013: 21 · 2012: 25
- FWCI
- 9.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based recommendation, collaborative filtering, hybrid recommendation, feature weighting, social network analysis, linear regression, item similarity, user preferences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review