中文

Journal of Applied Sciences >

2025 , Vol. 43 >Issue 2: 222 - 233

DOI: https://doi.org/10.3969/j.issn.0255-8297.2025.02.003

Communication Engineering

A Movie Rating Prediction Model Leveraging User Profile Similarity

Expand
  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Received date: 2022-12-09

  Online published: 2025-04-03

Fold

Abstract

Collaborative filtering algorithms are widely used in recommendation systems, with a key research focus on how to achieve effective user clustering and identify more accurate sets of similar neighbors. One of the key research focuses is to achieve user clustering and identify more similar neighbor sets. To enhance the accuracy of classification and prediction in these algorithms, this paper proposes a movie recommendation algorithm based on user profile similarity. First, based on a tag set of movie content features, a user preference profile matrix is constructed by calculating the frequency of user ratings among different movie content tags. Then, user similarity is calculated through this matrix, and a user complex network is modeled to determine the centrality weight of users within the network. Finally, the community weight is obtained through K-core decomposition of the user network. The rating predictions are improved by integrating the centrality weight and community weight of neighboring users. Experimental results show that the proposed algorithm improves prediction accuracy and classification accuracy by 2.72% and 3.17%, respectively. These results validate the effectiveness of complex network modeling based on user profile similarity for enhancing information utilization in recommendation systems.

Key words： user profile; collaborative filtering; similarity; complex network; classification accuracy

Cite this article

AI Jun, LI Minghao, SU Zhan . A Movie Rating Prediction Model Leveraging User Profile Similarity[J]. Journal of Applied Sciences, 2025 , 43(2) : 222 -233 . DOI: 10.3969/j.issn.0255-8297.2025.02.003

References

[1] Darban Z Z, Valipour M H. GHRS: graph-based hybrid recommendation system with application to movie recommendation [J]. Expert Systems with Applications, 2022, 200: 116850.
[2] Jena K K, Bhoi S K, Mallick C, et al. Neural model based collaborative filtering for movie recommendation system [J]. International Journal of Information Technology, 2022: 1-11.
[3] Zhang T T, Liu S N. Hybrid music recommendation algorithm based on music gene and improved knowledge graph [DB/OL]. 2022[2022-12-09]. https://doi.org/10.1155/2022/5889724.
[4] Schedl M, Knees P, Mcfee B, et al. Music recommendation systems: techniques, use cases, and challenges [M]//Recommender Systems Handbook, Springer, 2022.
[5] Wu C, Wu F, Qi T, et al. Feedrec: news feed recommendation with various user feedbacks [DB/OL]. 2021[2022-12-09]. https://arxiv.org/abs/2102.04903.
[6] Naghiaei M, Rahmani H A, Dehghan M. The unfairness of popularity bias in book recommendation [DB/OL]. 2022[2022-12-09]. https://doi.org/10.48550/arXiv.2202.13446.
[7] Gao C, Lei W, He X, et al. Advances and challenges in conversational recommender systems: a survey [DB/OL]. 2021[2022-12-09]. https://doi.org/10.48550/arXiv.2101.09459.
[8] 贾丹, 孙静宇. 基于用户会话的TF-Ranking推荐方法[J]. 应用科学学报, 2021, 39(3): 495-507. Jia D, Sun J Y. TF-Ranking recommendation method based on user session [J]. Journal of Applied Sciences, 2021, 39(3): 495-507. (in Chinese)
[9] Wu L, He X, Wang X, et al. A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(5): 4425-4445.
[10] Koren Y, Rendle S, Bell R. Advances in collaborative filtering [M]//Recommender Systems Handbook, Springer, 2022.
[11] Zhang F, Bai L, Gao F. A user trust-based collaborative filtering recommendation algorithm [C]//International Conference on Information and Communications Security, 2009: 411-424.
[12] Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms [C]//10th International Conference on World Wide Web, 2001: 285-295.
[13] Javed U, Shaukat K, Hameed I A, et al. A review of content-based and context-based recommendation systems [J]. International Journal of Emerging Technologies in Learning, 2021, 16(3): 274-306.
[14] Jalili M, Ahmadian S, Izadi M, et al. Evaluating collaborative filtering recommender algorithms: a survey [J]. IEEE Access, 2018, 6: 74003-74024.
[15] 季德强, 王海荣, 车淼. KNN-GWD推荐模型及其应用[J]. 应用科学学报, 2022, 40(1): 145-154. Ji D Q, Wang H R, Che M. KNN-GWD recommendation model and its application [J]. Journal of Applied Sciences, 2022, 40(1): 145-154. (in Chinese)
[16] Su X, Khoshgoftaar T M. A survey of collaborative filtering techniques [DB/OL]. 2009[2022-12-09]. https://doi.org/10.1155/2009/421425.
[17] Widiyaningtyas T, Hidayah I, Adji T B. User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system [J]. Journal of Big Data, 2021, 8(1): 1-21.
[18] Xu H, Hou R, Yang N, et al. Kitchen appliance recommendation based on user profile and interest evolution network [C]//IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021: 2096-2100.
[19] Pan Y, Huo Y, Tang J, et al. Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system [J]. Information Sciences, 2021, 545: 448-464.
[20] Li Z, Zhang L. Fast neighbor user searching for neighborhood-based collaborative filtering with hybrid user similarity measures [J]. Soft Computing, 2021, 25(7): 5323-5338.
[21] Polatidis N, Georgiadis C K. A multi-level collaborative filtering method that improves recommendations [J]. Expert Systems with Applications, 2016, 48: 100-110.
[22] Su Z, Lin Z, Ai J, et al. Rating prediction in recommender systems based on user behavior probability and complex network modeling [J]. IEEE Access, 2021, 9: 30739-30749.
[23] Ahmadian S, Afsharchi M, Meghdadi M. An effective social recommendation method based on user reputation model and rating profile enhancement [J]. Journal of Information Science, 2019, 45(5): 607-642.
[24] Jiang L C, Liu R R, Jia C X. User-location distribution serves as a useful feature in itembased collaborative filtering [J]. Physica A: Statistical Mechanics and Its Applications, 2022, 586: 126491.
[25] Singh P K, Sinha M, Das S, et al. Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item [J]. Applied Intelligence, 2020, 50(12): 4708-4731.
[26] Mehal A S, Meena K, Singh R B, et al. Movie genres and beyond: an analytical survey of classification techniques [C]//5th International Conference on Trends in Electronics and Informatics, 2021: 1193-1198.
[27] Harper F M, Konstan J A. The movielens datasets: history and context [J]. ACM Transactions on Interactive Intelligent Systems, 2015, 5(4): 1-19.
[28] De Campos L M, Fernández-Luna J M, Huete J F, et al. Group recommending: a methodological approach based on Bayesian networks [C]// IEEE 23rd International Conference on Data Engineering Workshop, 2007: 835-844.
[29] Ai J, Liu Y, Su Z, et al. K-core decomposition in recommender systems improves accuracy of rating prediction [J]. International Journal of Modern Physics C, 2021, 32(7): 2150087.
[30] Ai J, Liu Y, Su Z, et al. Link prediction in recommender systems based on multi-factor network modeling and community detection [J]. Europhysics Letters, 2019, 126(3): 38003.
[31] Jaramillo-Garzón J A, Castellanos-Domínguez C G. Improving protein sub-cellular localization prediction through semi-supervised learning [C]//16th International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies, 2014: 99-103.
[32] Ai J, Cai Y, Su Z, et al. Predicting user-item links in recommender systems based on similaritynetwork resource allocation [J]. Chaos, Solitons & Fractals, 2022, 158: 112032.
[33] Ai J, Li L, Su Z, et al. Online-rating prediction based on an improved opinion spreading approach [C]//29th Chinese Control and Decision Conference (CCDC), 2017: 1457-1460.
[34] Lee S. Using entropy for similarity measures in collaborative filtering [J]. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(1): 363-374.
[35] Madadipouya K, Chelliah S. A literature review on recommender systems algorithms, techniques and evaluations [J]. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 2017, 8(2): 109-124.
[36] Billsus D, Pazzani M J, et al. Learning collaborative information filters [C]//Recommender Systems Workshop, American Association for Artificial Intelligence (AAAI), 1998: 46-54.
[37] Castells P, Hurley N, Vargas S. Novelty and diversity in recommender systems [M]//Recommender Systems Handbook. Springer, 2022.
[38] Shani G, Gunawardana A. Evaluating recommendation systems [M]//Recommender Systems Handbook. Springer, 2011.
Options
Outlines

/

〈 〉