The DOI matches its Crossref record.
A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems
Open accessAbstract
Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 34
- K. Chopra, K. Gupta and A. Lambora, "Future internet: The internet of things-a literature review," IEEE -2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 135-139.
- T. R. Mahesh, V. Vivek and V. V. Kumar, "Recommendation Systems: Different Techniques, Challenges and Future Directions," International Journal of Information Technology, Research and Applications, vol. 1, no. 2, pp. 1-8, 2022.
- A. Da'u, N. Salim, I. Rabiu and A. Osman, "Recommendation system exploiting aspect-based opinion mining with deep learning method," Information Sciences, vol. 512, pp. 1279-1292, 2020.
- P. K. Singh, P. K. Pramanik and P. Choudhury, "Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends," Apple Academic Press, pp. 183-215, 2020.
- A. A. Amer and L. Nguyen, "Combinations of jaccard with numerical measures for collaborative filtering enhancement: Current work and future proposal," arXiv preprint arXiv:2111.12202, 2021. · arXiv
- V. Mohammadi, A. M. Rahmani and A. M. Darwesh, "Trust-based recommendation systems in Internet of Things: a systematic literature review," Human-centric Computing and Information Sciences, vol. 9, no. 1, pp. 1- 61, 2019.
- G. Jain, T. Mahara and S. C. Sharma, "Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique," Procedia Computer Science, vol. 218, pp. 1834-1844, 2023.
- G. R. Lima, C. E. Mello, A. Lyra and G. Zimbrao, "Applying landmarks to enhance memory-based collaborative filtering," Information Sciences, vol. 2020, no. 513, pp. 412-428, 2020.
- W. Yue, Z. Wang, B. Tian, M. Pook and X. Liu, "A hybrid model-and memory-based collaborative filtering algorithm for baseline data prediction of Friedreich's ataxia patients," IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 1428-1437, 2020.
- D. Valcarce, A. Landin, J. Parapar and Barreiro, "Collaborative filtering embeddings for memory-based recommender systems," Engineering Applications of Artificial Intelligence, vol. 85, pp. 347-356, 2019.
- K. Lin, N. Sonboli, B. Mobasher and R. Burke, "Calibration in collaborative filtering recommender systems: a user-centered analysis," Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 197-206, 2020.
- B.Nagesh, Ch.Harika, " Recommendation System for Find Friend on Social Networks," International Journal of Computer Engineering In Research Trends, vol 2,no 12, pp. 1188-1191 ,2015.
- J. P. Yaacoub, H. Noura, O. Salman and A. Chehab, "Security analysis of drones systems: Attacks, limitations, and recommendations," Internet of Things, vol. 11, p. 100218, 2020.
- S. Ojagh, M. R. Malek , S. Saeedi and S. Liang, "A location-based orientation-aware recommender system using IoT smart devices and Social Networks," Future Generation Computer Systems, vol. 108, pp. 97-118, 2020.
- C. Hansen, L. Maystre and R. Mehrotra, "Contextual and sequential user embeddings for large-scale music recommendation," Proceedings of the 14th ACM Conference on Recommender Systems, 2020.
- S. Gupta and V. Kant, "Credibility score based multi- criteria recommender system," Knowledge-Based Systems, vol. 196, p. 105756, 2020.
- D. Algawiaz, "Personalized Risk Aware Recommender Systems," Doctoral dissertation, ResearchSpace@ Auckland, 2020.
- S. R. Patil ," Hybridization Of Web Page Recommender Systems Based On Ml Techniques," International Journal of Computer Engineering In Research Trends, vol 6,no 5, pp. 310-316 ,2019.
- R. Logesh, V. Subramaniyaswamy and D. Malathi, "Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method," Neural Computing and Applications, vol. 32, pp. 2141-2164, 2020.
- M. F. Aljunid and D. H. Manjaiah, "Movie recommender system based on collaborative filtering using apache spark," Springer Singapore -Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, vol. 2, pp. 283-295, 2019.
- K. Han, "Personalized news recommendation and simulation based on improved collaborative filtering algorithm," Complexity, pp. 1-12, 2020.
- P. F. Cueto, M. Roet and A. Słowik, "Completing partial recipes using item-based collaborative filtering to recommend ingredients," arXiv preprint arXiv:1907.12380, 2019. · arXiv
- A.Kirankumar,P.Ganesh Kumar Reddy, A.Ram Charan Reddy, Baneti Shivaji and D.Jayanarayan Reddy," A Logic-based Friend Reference Semantic System for an online Social Networks," International Journal of Computer Engineering In Research Trends, vol 1,no 6, pp. 501-506 ,2014.
- B. Alhijawi and Y. Kilani, "A collaborative filtering recommender system using genetic algorithm," Information Processing & Management, vol. 57, no. 6, p. 102310, 2020.
- J. Mlyahilu and J. N. Kim, "Parametric and Non Parametric Measures for Text Similarity," Journal of the Institute of Convergence Signal Processing, vol. 20, no. 4, pp. 193-198, 2019.
- M. Kirişci, "New cosine similarity and distance measures for Fermatean fuzzy sets and TOPSIS approach," Knowledge and Information Systems, pp. 1-14, 2022.
- N.Satish Kumar, Sujan Babu Vadde," Typicality Based Content-Boosted Collaborative Filtering Recommendation Framework," International Journal of Computer Engineering In Research Trends, vol 2,no 11, pp. 809-813 ,2015.
- X. Wang, Y. Ran and T. Jia, "Measuring similarity in co- occurrence data using ego-networks," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 1, p. 013101, 2020.
- Yedukondalu, Gangolu & K., Samunnisa & Bhavsingh, M. & Raghuram, I & Addepalli, Lavanya," MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm",International Journal on Recent and Innovation Trends in Computing and Communication,Vol 10,no 10. Pp.143-154, 2022. 10
- J. Wang and Y. Dong, "Measurement of text similarity: a survey," Information, vol. 11, no. 9, p. 421, 2020.
- S. A. Shishavan, F. K. Gündoğdu and K. Farrokhizade, "Novel similarity measures in spherical fuzzy environment and their applications," Engineering Applications of Artificial Intelligence, vol. 94, p. 103837, 2020.
- Y. Xu and P. Wang, "An enhanced squared exponential kernel with Manhattan similarity measure for high dimensional Gaussian process models.," nternational Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 85390 , p. V03BT03A025, 2021.
- A. Golovanov, A. Kupavskii and A. Sagdeev, "Odd- distance and right-equidistant sets in the maximum and Manhattan metrics," European Journal of Combinatorics, vol. 107, p. 103603, 2023.
- Joy and V. G. Renumol, "Comparison of generic similarity measures in E-learning content recommender system in cold-start condition," 2020 IEEE Bombay section signature conference, pp. 175-179, 2020.
Cited by
Sorted by citations
Pardede, J., Yudistira, A., & Yudistira, A. (2025) · Ultimatics: Jurnal Teknik Informatika · doi:10.31937/ti.v17i1.3867
Metadata
- Authors
- Kumar, P., Gupta, M. K., Rao, C. R. S., Bhavsingh, M., & Srilakshmi, M.
- Affiliations
- Gyan Vihar University (IN)
- Venue
- International Journal on Recent and Innovation Trends in Computing and Communication
- Publisher
- Auricle Technologies, Pvt., Ltd.
- Published
- 2023-03-11
- Volume
- 11
- Issue
- 3s
- Pages
- 184-192
- ISSN
- 2321-8169 (ISSN-L)
- OpenAlex
- W4367321484
- Cited by
- 12
- References
- 0
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 4 · 2023: 1
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- other-oa
- Locations
- International Journal on Recent and Innovation Trends in Computing and Communication (publishedVersion, other-oa)
- 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
- collaborative filtering, similarity measures, recommender systems, user-based collaborative filtering, item-based collaborative filtering, content-based filtering, hybrid recommendation system, personalized recommendation
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. https://app.cochat.ai/papers/10.17762/ijritcc.v11i3s.6180
- The paper
- Kumar, P., Gupta, M. K., Rao, C. R. S., Bhavsingh, M., & Srilakshmi, M. (2023). A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. International Journal on Recent and Innovation Trends in Computing and Communication. https://doi.org/10.17762/ijritcc.v11i3s.6180
