-
CiteScore
-
Impact Factor
IECE Transactions on Sensing, Communication, and Control, 2024, Volume 1, Issue 1: 30-51

Free Access | Review Article | 15 October 2024
1 Collaborative Innovation Center for Common Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2 Department of Philosophy, Sociology, Education, and Applied Psychology, University of Padua, Padova 35139, Italy
3 Department of Human, Philosophic and Education Sciences, University of Salerno, Fisciano 84084, Italy
* Corresponding author: Yingxin Tan, email: yingxin.tan@studenti.unipd.it
Received: 03 September 2024, Accepted: 03 October 2024, Published: 15 October 2024  

Abstract
The proliferation of Recommender Systems (RecSys), driven by their expanding application domains, explosive data growth, and exponential advancements in computing capabilities, has cultivated a dynamic and evolving research landscape. This paper comprehensively reviews the foundational concepts, methodologies, and challenges associated with RecSys from technological and social scientific lenses. Initially, it categorizes personalized RecSys technical solutions into five paradigms: collaborative filtering, scenario-aware, knowledge & data co-driven approaches, large language models, and hybrid models integrating diverse data sources. Subsequently, the paper analyses the key challenges and future trajectories in five technical domains: general technologies, recommendation accuracy, cold-start problems, explainability, and privacy protection. The review also explores the intersection between RecSys and social sciences, emphasizing how RecSys is shaped by and, in turn, shapes social structures, cultural norms, and societal biases, alongside its influence on decision-making, behaviour, and identity formation. Identified research gaps highlight the need for deeper investigations into cross-cultural variations and long-term effects, as well as for integrating sociological and psychological insights with technical designs. This review systematically encapsulates the current research landscape of RecSys across technological and sociological domains, thereby guiding researchers toward identifying potential advancements and future research directions.

Graphical Abstract
Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications

Keywords
recommender system
personalized recommendation
technological roadmap
sociological intersections
psychological implications

References

[1]Ricci, F., Rokach, L., & Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Boston, MA: springer US.

[2]Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3), 77-87.

[3]Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12, 331-370.

[4]Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38.

[5]Pan, W., Xiang, E., Liu, N., & Yang, Q. (2010, July). Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 24, No. 1, pp. 230-235).

[6]Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002, August). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 253-260).

[7]Ramadhan, G., & Setiawan, E. B. (2022, November). Collaborative filtering recommender system based on memory based in twitter using decision tree learning classification (case study: Movie on netflix). In 2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) (pp. 1-6). IEEE.

[8]Rifai, A. F., & Setiawan, E. B. (2022). Memory-based collaborative filtering on twitter using support vector machine classification. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(5), 702-709.

[9]Valcarce, D., Landin, A., Parapar, J., & Barreiro, Á. (2019). Collaborative filtering embeddings for memory-based recommender systems. Engineering Applications of Artificial Intelligence, 85, 347-356.

[10]Chen, C. M., Wang, C. J., Tsai, M. F., & Yang, Y. H. (2019, May). Collaborative similarity embedding for recommender systems. In The World Wide Web Conference (pp. 2637-2643).

[11]Barkan, O., Hirsch, R., Katz, O., Caciularu, A., & Koenigstein, N. (2021, October). Anchor-based collaborative filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 2877-2881).

[12]Malandri, L., Porcel, C., Xing, F., Serrano-Guerrero, J., & Cambria, E. (2022). Soft computing for recommender systems and sentiment analysis. Applied Soft Computing, 118, 108246.

[13]Zheng, L., Noroozi, V., & Yu, P. S. (2017, February). Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the tenth ACM international conference on web search and data mining (pp. 425-434).

[14]Seo, S., Huang, J., Yang, H., & Liu, Y. (2017, August). Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In Proceedings of the eleventh ACM conference on recommender systems (pp. 297-305).

[15]Chin, J. Y., Zhao, K., Joty, S., & Cong, G. (2018, October). ANR: Aspect-based neural recommender. In Proceedings of the 27th ACM International conference on information and knowledge management (pp. 147-156).

[16]Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., & Wu, L. (2019, July). A capsule network for recommendation and explaining what you like and dislike. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 275-284).

[17]Wu, C., Wu, F., An, M., Huang, J., Huang, Y., & Xie, X. (2019, July). NPA: neural news recommendation with personalized attention. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2576-2584).

[18]Liu, N., Ge, Y., Li, L., Hu, X., Chen, R., & Choi, S. H. (2020, October). Explainable recommender systems via resolving learning representations. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 895-904).

[19]Fang, L., Du, B., & Wu, C. (2022). Differentially private recommender system with variational autoencoders. Knowledge-Based Systems, 250, 109044.

[20]Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., & Xie, X. (2021, July). Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 726-735).

[21]Xia, J., Li, D., Gu, H., Lu, T., Zhang, P., & Gu, N. (2021, October). Incremental graph convolutional network for collaborative filtering. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 2170-2179).

[22]Lin, Z., Tian, C., Hou, Y., & Zhao, W. X. (2022, April). Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In Proceedings of the ACM web conference 2022 (pp. 2320-2329).

[23]Zhao, M., Wu, L., Liang, Y., Chen, L., Zhang, J., Deng, Q., ... & Wu, R. (2022, July). Investigating accuracy-novelty performance for graph-based collaborative filtering. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (pp. 50-59).

[24]Huang, L., Yang, Y., Chen, H., Zhang, Y., Wang, Z., & He, L. (2022). Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data. Knowledge-Based Systems, 245, 108596.

[25]del Carmen Rodríguez-Hernández, M., & Ilarri, S. (2021). AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge-Based Systems, 215, 106740.

[26]Mei, L., Ren, P., Chen, Z., Nie, L., Ma, J., & Nie, J. Y. (2018, October). An attentive interaction network for context-aware recommendations. In Proceedings of the 27th ACM international conference on information and knowledge management (pp. 157-166).

[27]Unger, M., & Tuzhilin, A. (2020). Hierarchical latent context representation for context-aware recommendations. IEEE Transactions on Knowledge and Data Engineering, 34(7), 3322-3334.

[28]Unger, M., Shapira, B., Rokach, L., & Bar, A. (2017, July). Inferring contextual preferences using deep auto-encoding. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 221-229).

[29]Ouyang, Y., Wu, P., & Pan, L. (2022, October). Asymmetrical context-aware modulation for collaborative filtering recommendation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1595-1604).

[30]Nawara, D., & Kashef, R. (2022, April). Context-aware recommendation systems using consensus-clustering. In 2022 IEEE International Systems Conference (SysCon) (pp. 1-8). IEEE.

[31]Ebesu, T., & Fang, Y. (2017, August). Neural citation network for context-aware citation recommendation. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval (pp. 1093-1096).

[32]Al Jawarneh, I. M., Bellavista, P., Corradi, A., Foschini, L., Montanari, R., Berrocal, J., & Murillo, J. M. (2020). A pre-filtering approach for incorporating contextual information into deep learning based recommender systems. IEEE Access, 8, 40485-40498.

[33]Wu, J., He, X., Wang, X., Wang, Q., Chen, W., Lian, J., & Xie, X. (2022). Graph convolution machine for context-aware recommender system. Frontiers of Computer Science, 16(6), 166614.

[34]Deldjoo, Y., Schedl, M., Cremonesi, P., & Pasi, G. (2020). Recommender systems leveraging multimedia content. ACM Computing Surveys (CSUR), 53(5), 1-38.

[35]Van Dat, N., Van Toan, P., & Thanh, T. M. (2022). Solving distribution problems in content-based recommendation system with gaussian mixture model. Applied Intelligence, 52(2), 1602-1614.

[36]Yang, Y., Zhu, Y., & Li, Y. (2022). Personalized recommendation with knowledge graph via dual-autoencoder. Applied Intelligence, 52(6), 6196-6207.

[37]Deldjoo, Y., Elahi, M., Quadrana, M., & Cremonesi, P. (2018). Using visual features based on MPEG-7 and deep learning for movie recommendation. International journal of multimedia information retrieval, 7, 207-219.

[38]Cami, B. R., Hassanpour, H., & Mashayekhi, H. (2019). User preferences modeling using dirichlet process mixture model for a content-based recommender system. Knowledge-Based Systems, 163, 644-655.

[39]Wang, C., Zhou, T., Chen, C., Hu, T., & Chen, G. (2019, July). CAMO: A collaborative ranking method for content based recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 5224-5231).

[40]Serrano-Guerrero, J., Olivas, J. A., & Romero, F. P. (2020). A T1OWA and aspect-based model for customizing recommendations on eCommerce. Applied Soft Computing, 97, 106768.

[41]Polignano, M., Narducci, F., de Gemmis, M., & Semeraro, G. (2021). Towards emotion-aware recommender systems: an affective coherence model based on emotion-driven behaviors. Expert Systems with Applications, 170, 114382.

[42]Amato, F., Moscato, V., Picariello, A., & Piccialli, F. (2019). SOS: a multimedia recommender system for online social networks. Future generation computer systems, 93, 914-923.

[43]Zhang, Y., Shen, G., Han, X., Wang, W., & Kong, X. (2022). Spatio-temporal digraph convolutional network-based taxi pickup location recommendation. IEEE Transactions on Industrial Informatics, 19(1), 394-403.

[44]Ma, X., Zhang, Y., & Zeng, J. (2019). Newly published scientific papers recommendation in heterogeneous information networks. Mobile Networks and Applications, 24, 69-79.

[45]Pham, P., Nguyen, L. T., Nguyen, N. T., Kozma, R., & Vo, B. (2023). A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendation. Information Sciences, 620, 105-124.

[46]Chen, Q., Lin, J., Zhang, Y., Ding, M., Cen, Y., Yang, H., & Tang, J. (2019). Towards knowledge-based recommender dialog system. arXiv preprint arXiv:1908.05391.

[47]Anelli, V. W., Di Noia, T., Di Sciascio, E., Ferrara, A., & Mancino, A. C. M. (2021, September). Sparse feature factorization for recommender systems with knowledge graphs. In Proceedings of the 15th ACM Conference on Recommender Systems (pp. 154-165).

[48]Huang, R., Han, C., & Cui, L. (2021, October). Entity-aware collaborative relation network with knowledge graph for recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3098-3102).

[49]Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., ... & Bo, L. (2021, May). Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 5, pp. 4486-4493).

[50]Chen, L., Li, Z., Xu, T., Wu, H., Wang, Z., Yuan, N. J., & Chen, E. (2022, August). Multi-modal siamese network for entity alignment. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (pp. 118-126).

[51]Vijayakumar, V., Vairavasundaram, S., Logesh, R., & Sivapathi, A. (2019). Effective knowledge based recommender system for tailored multiple point of interest recommendation. International Journal of Web Portals (IJWP), 11(1), 1-18.

[52]Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., ... & Ye, Y. (2021, May). Knowledge-aware coupled graph neural network for social recommendation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 5, pp. 4115-4122).

[53]Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., & Chua, T. S. (2021, April). Learning intents behind interactions with knowledge graph for recommendation. In Proceedings of the web conference 2021 (pp. 878-887).

[54]Liu, D., Lian, J., Liu, Z., Wang, X., Sun, G., & Xie, X. (2021, August). Reinforced anchor knowledge graph generation for news recommendation reasoning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1055-1065).

[55]Gong, F., Wang, M., Wang, H., Wang, S., & Liu, M. (2021). SMR: medical knowledge graph embedding for safe medicine recommendation. Big Data Research, 23, 100174.

[56]Abu-Salih, B., Alsawalqah, H., Elshqeirat, B., Issa, T., & Wongthongtham, P. (2019). Toward a knowledge-based personalised recommender system for mobile app development. arXiv preprint arXiv:1909.03733.

[57]Cui, L., & Lee, D. (2022, July). Ketch: Knowledge graph enhanced thread recommendation in healthcare forums. In Proceedings of the 45th international acm sigir conference on research and development in information retrieval (pp. 492-501).

[58]Gao, Y., Sheng, T., Xiang, Y., Xiong, Y., Wang, H., & Zhang, J. (2023). Chat-rec: Towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524.

[59]Friedman, L., Ahuja, S., Allen, D., Tan, Z., Sidahmed, H., Long, C., ... & Tiwari, M. (2023). Leveraging large language models in conversational recommender systems. arXiv preprint arXiv:2305.07961.

[60]Bao, K., Zhang, J., Wang, W., Zhang, Y., Yang, Z., Luo, Y., ... & Tian, Q. (2023). A bi-step grounding paradigm for large language models in recommendation systems. arXiv preprint arXiv:2308.08434.

[61]Chu, Z., Hao, H., Ouyang, X., Wang, S., Wang, Y., Shen, Y., ... & Li, S. (2023). Leveraging large language models for pre-trained recommender systems. arXiv preprint arXiv:2308.10837.

[62]Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2024, March). Large language models are zero-shot rankers for recommender systems. In European Conference on Information Retrieval (pp. 364-381).

[63]Wang, Y., Chu, Z., Ouyang, X., Wang, S., Hao, H., Shen, Y., ... & Li, S. (2023). Enhancing recommender systems with large language model reasoning graphs. arXiv preprint arXiv:2308.10835.

[64]Carranza, A. G., Farahani, R., Ponomareva, N., Kurakin, A., Jagielski, M., & Nasr, M. (2023). Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. arXiv preprint arXiv:2305.05973.

[65]Kiran, R., Kumar, P., & Bhasker, B. (2020). DNNRec: A novel deep learning based hybrid recommender system. Expert Systems with Applications, 144, 113054.

[66]Jolfaei, A. A., Aghili, S. F., & Singelee, D. (2021). A survey on blockchain-based IoMT systems: Towards scalability. IEEE Access, 9, 148948-148975.

[67]Polignano, M., Musto, C., de Gemmis, M., Lops, P., & Semeraro, G. (2021, September). Together is better: Hybrid recommendations combining graph embeddings and contextualized word representations. In Proceedings of the 15th ACM conference on recommender systems (pp. 187-198).

[68]Luo, S., Zhang, X., Xiao, Y., & Song, L. (2022, October). HySAGE: A hybrid static and adaptive graph embedding network for context-drifting recommendations. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1389-1398).

[69]Khatter, H., Goel, N., Gupta, N., & Gulati, M. (2021, September). Movie recommendation system using cosine similarity with sentiment analysis. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 597-603). IEEE.

[70]He, X., He, Z., Du, X., & Chua, T. S. (2018, June). Adversarial personalized ranking for recommendation. In The 41st International ACM SIGIR conference on research & development in information retrieval (pp. 355-364).

[71]Tang, J., Du, X., He, X., Yuan, F., Tian, Q., & Chua, T. S. (2019). Adversarial training towards robust multimedia recommender system. IEEE Transactions on Knowledge and Data Engineering, 32(5), 855-867.

[72]Christakopoulou, K., & Banerjee, A. (2019, September). Adversarial attacks on an oblivious recommender. In Proceedings of the 13th ACM Conference on Recommender Systems (pp. 322-330).

[73]Du, Y., Fang, M., Yi, J., Xu, C., Cheng, J., & Tao, D. (2018). Enhancing the robustness of neural collaborative filtering systems under malicious attacks. IEEE Transactions on Multimedia, 21(3), 555-565.

[74]Yue, Z., Zeng, H., Kou, Z., Shang, L., & Wang, D. (2022, September). Defending substitution-based profile pollution attacks on sequential recommenders. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 59-70).

[75]Li, J., Ren, Y., & Deng, K. (2022, April). FairGAN: GANs-based fairness-aware learning for recommendations with implicit feedback. In Proceedings of the ACM web conference 2022 (pp. 297-307).

[76]Xie, Y., Wang, Z., Yang, C., Li, Y., Ding, B., Deng, H., & Han, J. (2022, April). Komen: Domain knowledge guided interaction recommendation for emerging scenarios. In Proceedings of the ACM Web Conference 2022 (pp. 1301-1310).

[77]Huang, J., Oosterhuis, H., & De Rijke, M. (2022, February). It is different when items are older: Debiasing recommendations when selection bias and user preferences are dynamic. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 381-389).

[78]Shi, L., Li, S., Ding, X., & Bu, Z. (2023). Selection bias mitigation in recommender system using uninteresting items based on temporal visibility. Expert Systems with Applications, 213, 118932.

[79]Wang, Z., Shen, S., Wang, Z., Chen, B., Chen, X., & Wen, J. R. (2022, April). Unbiased sequential recommendation with latent confounders. In Proceedings of the ACM Web Conference 2022 (pp. 2195-2204).

[80]Zhou, C., Ma, J., Zhang, J., Zhou, J., & Yang, H. (2021, August). Contrastive learning for debiased candidate generation in large-scale recommender systems. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 3985-3995).

[81]Wang, X., Zhang, R., Sun, Y., & Qi, J. (2021, March). Combating selection biases in recommender systems with a few unbiased ratings. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (pp. 427-435).

[82]Liu, H., Lin, H., Fan, W., Ren, Y., Xu, B., Zhang, X., ... & Yang, L. (2022). Self-supervised learning for fair recommender systems. Applied Soft Computing, 125, 109126.

[83]Lee, H., Im, J., Jang, S., Cho, H. & Chung, S. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. Proceedings Of The 25th ACM SIGKDD International Conference On Knowledge Discovery & Data Mining. pp. 1073-1082 (2019,7)

[84]Shivaswamy, P., & Garcia-Garcia, D. (2022, September). Adversary or friend? an adversarial approach to improving recommender systems. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 369-377).

[85]Liu, S., Ge, Y., Xu, S., Zhang, Y., & Marian, A. (2022, September). Fairness-aware federated matrix factorization. In Proceedings of the 16th ACM conference on recommender systems (pp. 168-178).

[86]Do, V., Corbett-Davies, S., Atif, J., & Usunier, N. (2022, June). Online certification of preference-based fairness for personalized recommender systems. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 6, pp. 6532-6540).

[87]Gupta, P., Sharma, A., Malhotra, P., Vig, L., & Shroff, G. (2021, October). Causer: Causal session-based recommendations for handling popularity bias. In Proceedings of the 30th ACM international conference on information & knowledge management (pp. 3048-3052).

[88]Anelli, V. W., Di Noia, T., & Merra, F. A. (2021, September). The idiosyncratic effects of adversarial training on bias in personalized recommendation learning. In Proceedings of the 15th ACM Conference on Recommender Systems (pp. 730-735).

[89]Ji, L., Qin, Q., Han, B., & Yang, H. (2021, October). Reinforcement learning to optimize lifetime value in cold-start recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 782-791).

[90]Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015, May). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on World Wide Web (pp. 111-112).

[91]Muresan, M., Fu, L., & Pan, G. (2019). Adaptive traffic signal control with deep reinforcement learning an exploratory investigation. arXiv preprint arXiv:1901.00960.

[92]Li, H., Kumar, N., Chen, R., & Georgiou, P. (2018, April). A deep reinforcement learning framework for Identifying funny scenes in movies. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3116-3120). IEEE.

[93]Xiong, W., Hoang, T., & Wang, W. Y. (2017). Deeppath: A reinforcement learning method for knowledge graph reasoning. arXiv preprint arXiv:1707.06690.

[94]Chen, X., Li, S., Li, H., Jiang, S., Qi, Y., & Song, L. (2019, May). Generative adversarial user model for reinforcement learning based recommendation system. In International Conference on Machine Learning (pp. 1052-1061). PMLR.

[95]Zou, L., Xia, L., Du, P., Zhang, Z., Bai, T., Liu, W., ... & Yin, D. (2020, January). Pseudo Dyna-Q: A reinforcement learning framework for interactive recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 816-824).

[96]Wang, K., Zou, Z., Deng, Q., Tao, J., Wu, R., Fan, C., ... & Cui, P. (2021, May). Reinforcement learning with a disentangled universal value function for item recommendation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 5, pp. 4427-4435).

[97]Zhao, X., Xia, L., Zou, L., Liu, H., Yin, D., & Tang, J. (2020, October). Whole-chain recommendations. In \emph{Proceedings of the 29th ACM international conference on information & knowledge management (pp. 1883-1891).

[98]Montazeralghaem, A., & Allan, J. (2022, August). Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1275-1283).

[99]Bai, X., Guan, J., & Wang, H. (2019). A model-based reinforcement learning with adversarial training for online recommendation. Advances in Neural Information Processing Systems, 32.

[100]Hong, D., Li, Y., & Dong, Q. (2020, July). Nonintrusive-sensing and reinforcement-learning based adaptive personalized music recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1721-1724).

[101]Zhang, J. D., Chow, C. Y., & Xu, J. (2016). Enabling kernel-based attribute-aware matrix factorization for rating prediction. IEEE Transactions on Knowledge and Data Engineering, 29(4), 798-812.

[102]Xu, X., & Yuan, D. (2017, April). A novel matrix factorization recommendation algorithm fusing social trust and behaviors in micro-blogs. In 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 283-287). IEEE.

[103]Yi, S., & Zorzi, M. (2021). Robust kalman filtering under model uncertainty: The case of degenerate densities. IEEE Transactions on Automatic Control, 67(7), 3458-3471.

[104]Yi, S., & Zorzi, M. (2023). Robust fixed-lag smoothing under model perturbations. Journal of the Franklin Institute, 360(1), 458-483.

[105]Xu, J., Wei, L., Zhang, Y., Wang, A., Zhou, F., & Gao, C. Z. (2018). Dynamic fully homomorphic encryption-based merkle tree for lightweight streaming authenticated data structures. Journal of Network and Computer Applications, 107, 113-124.

[106]Park, H., Jeon, H., Kim, J., Ahn, B., & Kang, U. (2017). Uniwalk: Explainable and accurate recommendation for rating and network data. arXiv preprint arXiv:1710.07134.

[107]Kouki, P., Schaffer, J., Pujara, J., O'Donovan, J., & Getoor, L. (2017, August). User preferences for hybrid explanations. In Proceedings of the Eleventh ACM Conference on Recommender Systems (pp. 84-88).

[108]Milosevic, N., Gregson, C., Hernandez, R., & Nenadic, G. (2019). A framework for information extraction from tables in biomedical literature. International Journal on Document Analysis and Recognition (IJDAR), 22, 55-78.

[109]Wang, C., Ma, X., Chen, J., & Chen, J. (2018). Information extraction and knowledge graph construction from geoscience literature. Computers & geosciences, 112, 112-120.

[110]Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE transactions on knowledge and data engineering, 29(12), 2724-2743.

[111]Kim, S., Kim, J., Koo, D., Kim, Y., Yoon, H., & Shin, J. (2016, May). Efficient privacy-preserving matrix factorization via fully homomorphic encryption. In Proceedings of the 11th ACM on Asia conference on computer and communications security (pp. 617-628).

[112]Badsha, S., Yi, X., Khalil, I., Liu, D., Nepal, S., & Lam, K. Y. (2018). Privacy preserving user based web service recommendations. IEEE Access, 6, 56647-56657.

[113]Zhang, S., Li, X., Liu, H., Lin, Y., & Sangaiah, A. K. (2018). A privacy-preserving friend recommendation scheme in online social networks. Sustainable cities and society, 38, 275-285.

[114]Baglioni, E., Becchetti, L., Bergamini, L., Colesanti, U., Filipponi, L., Vitaletti, A., & Persiano, G. (2010, September). A lightweight privacy preserving SMS-based recommendation system for mobile users. In Proceedings of the fourth ACM Conference on Recommender systems (pp. 191-198).

[115]Liu, X., Choo, K. K. R., Deng, R. H., Lu, R., & Weng, J. (2016). Efficient and privacy-preserving outsourced calculation of rational numbers. IEEE Transactions on Dependable and Secure Computing, 15(1), 27-39.

[116]Halevi, S., & Shoup, V. (2018, July). Faster homomorphic linear transformations in HElib. In Annual International Cryptology Conference (pp. 93-120). Cham: Springer International Publishing.

[117]Zhou, J., Cao, Z., Dong, X., & Vasilakos, A. V. (2017). Security and privacy for cloud-based IoT: Challenges. IEEE Communications Magazine, 55(1), 26-33.

[118]Gillespie, T., Boczkowski, P. J., & Foot, K. A. (Eds.). (2014). Media technologies: Essays on communication, materiality, and society. MIT Press, pp. 167-193.

[119]Areeb, Q., Nadeem, M., Sohail, S., Imam, R., Doctor, F., Himeur, Y., Hussain, A. & Amira, A. (2023). Filter bubbles in recommender systems: Fact or fallacy—A systematic review. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 13(6), e1512.

[120]Ferraro, A., Ferreira, G., Diaz, F. & Born, G. (2022). Measuring commonality in recommendation of cultural content: Recommender systems to enhance cultural citizenship. Proceedings Of The 16th ACM Conference On Recommender Systems (pp. 567-572).

[121]Ross Arguedas, A., Robertson, C., Fletcher, R., & Nielsen, R. (2022). Echo chambers, filter bubbles, and polarisation: A literature review.

[122]Cinus, F., Minici, M., Monti, C. & Bonchi, F. (2022). The effect of people recommenders on echo chambers and polarization. Proceedings Of The International AAAI Conference On Web And Social Media, (Vol. 16, pp. 90-101).

[123]Beer, D. (2019). The social power of algorithms. In The Social Power of Algorithms (pp. 1-13). Routledge.

[124]Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public opinion quarterly, 80(S1), 298-320.

[125]Whitcomb, C. G. (2020). Review of Shoshana Zuboff (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power: New York: Public Affairs. 704 pp. ISBN 9781781256848 (Hardcover). Postdigital Science and Education, 2, 484-488.

[126]Bucher, T. (2018). If... then: Algorithmic power and politics. Oxford University Press.

[127]Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. In Algorithms of oppression. New York university press.

[128]Pitoura, E., Stefanidis, K. & Koutrika, G. (2022). Fairness in rankings and recommendations: an overview. The VLDB Journal. pp. 1-28.

[129]Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., \& Zanzonelli, D. (2024). Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction, 34(1), 59-108.

[130]Simonson, I., & Rosen, E. (2014). What marketers misunderstand about online reviews. Harvard Business Review, 92(1), 7.

[131]Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., & Riedl, J. (2003, April). Is seeing believing? How recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 585-592).

[132]Milano, S., Taddeo, M., & Floridi, L. (2020). Recommender systems and their ethical challenges. Ai & Society, 35, 957-967.

[133]Mu, R. (2018). A survey of recommender systems based on deep learning. IEEE Access, 6, 69009-69022.

[134]Ickes, J. L. (2010). Nudge: Improving decisions about health, wealth, and happiness. The Journal of Applied Christian Leadership, 4(1), 133.

[135]Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction, 22, 101-123.

[136]Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, 7(1), 76-80.

[137]Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User modeling and user-adapted interaction, 22, 441-504.

[138]Falconnet, A., Coursaris, C., Beringer, J., Van Osch, W., Sénécal, S. & Léger, P. (2023). Improving User Experience with Recommender Systems by Informing the Design of Recommendation Messages. Applied Sciences, 13(4), 2706.

[139]Roy, D. & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal Of Big Data, 9(1), 59.

[140]Lex, E., Kowald, D., Seitlinger, P., Tran, T., Felfernig, A., & Schedl, M. (2021). Psychology-informed recommender systems. Foundations And Trends® In Information Retrieval. 15(2), 134-242.

[141]Gürses, S., & Diaz, C. (2013). Two tales of privacy in online social networks. IEEE Security & Privacy, 11(3), 29-37.

[142]Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W. & Starnini, M. (2021). The echo chamber effect on social media. Proceedings Of The National Academy Of Sciences, 118(9), e2023301118.

[143]Valkenburg, P. M., Meier, A., & Beyens, I. (2022). Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Current opinion in psychology, 44, 58-68.

[144]Chen, Y., Harper, F. M., Konstan, J., & Li, S. X. (2010). Social comparisons and contributions to online communities: A field experiment on movielens. American Economic Review, 100(4), 1358-1398.


Cite This Article
APA Style
An, Y., Tan, Y., Sun, X., & Ferrari, G. (2024). Recommender System: A Comprehensive Overview of Technical Challenges and Social Implications. IECE Transactions on Sensing, Communication, and Control, 1(1), 30–51. https://doi.org/10.62762/TSCC.2024.898503

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 158
PDF Downloads: 37

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Sensing, Communication, and Control

IECE Transactions on Sensing, Communication, and Control

ISSN: request pending (Online) | ISSN: request pending (Print)

Email: jinxuebo@btbu.edu.cn

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.