A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems

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Praveen Kumar
Mukesh Kumar Gupta
Channapragada Rama Seshagiri Rao
M Bhavsingh
M. Srilakshmi


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.

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How to Cite
Kumar, P. ., M. K. . Gupta, C. R. S. . Rao, M. . Bhavsingh, and M. Srilakshmi. “A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 3s, Mar. 2023, pp. 184-92, https://ijritcc.org/index.php/ijritcc/article/view/6180.


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.

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. A. Olvera and T. B. Núnez, "Design a product recomendation model," 2022.

M. N. Ballesteros Carretero, "Research on recommender systems: A bibliometric study," 2021.

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.

V. Lytvyn, V. Vysotska, V. Shatskykh and I. Kohut, "Design of a recommendation system based on Collaborative Filtering and machine learning considering personal needs of the user," 2019.

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. S?owik, "Completing partial recipes using item-based collaborative filtering to recommend ingredients," arXiv preprint arXiv:1907.12380, 2019.

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.

H. Kusniyati and A. A. Nugraha, "Analysis of Matric Product Matching Between Cosine Similarity with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec in PT," Pricebook Digital Indonesia, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technology, 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. Kiri?ci, "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. 10.17762/ijritcc.v10i10.5743.

J. Wang and Y. Dong, "Measurement of text similarity: a survey," Information, vol. 11, no. 9, p. 421, 2020.

S. A. Shishavan, F. K. Gündo?du 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.

M. T. Hanif, M. Yongtong and S. B. Shah, "Economics of open-access fisheries: A case of factors affecting the revenue of coastal or inshore longline fisheries in Pakistan," 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.