Recommendation System Issues, Approaches and Challenges Based on User Reviews
DOI:
https://doi.org/10.13052/jwe1540-9589.2143Keywords:
Recommender system, Collaborative filtering, Content filtering, User reviews, SurveyAbstract
With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users’ trust.
Downloads
References
P. Nagarnaik and A. Thomas, “Survey on recommendation system methods,” in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, 2015, pp. 1603b 1608. https://doi.org/10.1109/ECS.2015.7124857.
Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities,” Appl. Sci. (Basel), vol. 10, no. 21, p. 7748, 2020.
R. Katarya and O. P. Verma, “An effective collaborative movie recommender system with cuckoo search,” Egyptian Informatics Journal, vol. 18, no. 2, pp. 105–112, 2017.
N. Idrissi, A. Zellou, O. Hourrane, Z. Bakkoury, and E. H. Benlahmar, “A New Hybrid-Enhanced Recommender System for Mitigating Cold Start Issues,” in Proceedings of the 2019 11th International Conference on Information Management and Engineering, London United Kingdom, 2019, pp. 10–14.
P. Jariha and S. K. Jain, “A state-of-the-art Recommender Systems: An overview on Concepts, Methodology and Challenges,” in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, 2018, pp. 1769–1774.
J. Valverde-Berrocoso, M. C. Garrido-Arroyo, C. Burgos-Videla, and M. B. Morales-Cevallos, “M. del C. Garrido-Arroyo, C. Burgos-Videla, and M. B. Morales-Cevallos, “Trends in Educational Research about e-Learning: A Systematic Literature Review (2009–2018),” Sustainability (Basel), vol. 12, no. 12, p. 5153, 2020.
J. Konaté, A. G. Diarra, S. O. Diarra, and A. Diallo, “SyrAgri: A Recommender System for Agriculture in Mali,” Information (Basel), vol. 11, no. 12, p. 561, 2020.
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.
X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell. vol. 2009, pp. 1–19, 2009.
K. Nageswara Rao, “Application Domain and Functional Classification of Recommender Systems - A Survey,” DESIDOC J. Libr. Inf. Technol., vol. 28, no. 3, pp. 17–35, 2008.
R. G. Lumauag, A. M. Sison, and R. P. Medina, “An Enhanced Recommendation Algorithm Based on Modified User-Based Collaborative Filtering,” in 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 198–202. https://doi.org/10.1109/CCOMS.2019.8821741.
N. Idrissi, A. Zellou, O. Hourrane, Z. Bakkoury, and E. H. Benlahmar, “Addressing Cold Start Challenges In Recommender Systems: Towards A New Hybrid Approach,” in 2019 International Conference on Smart Applications, Communications and Networking (SmartNets), Sharm El Sheik, Egypt, 2019, pp. 1–6.
A. A. Kardan and M. Ebrahimi, “A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups,” Inf. Sci., vol. 219, pp. 93–110, 2013.
L. Liao and F. Köttig, “A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction,” Appl. Soft Comput., vol. 44, pp. 191–199, 2016.
S. Gong, H. Ye, and H. Tan, “Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System,” in 2009 Pacific-Asia Conference on Circuits, Communications and Systems, Chengdu, China, 2009, pp. 690–693.
A. Suglia, C. Greco, C. Musto, M. de Gemmis, P. Lops, and G. Semeraro, “A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks,” in Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Bratislava Slovakia, 2017, pp. 202–211.
Z. Huang, C. Yu, J. Ni, H. Liu, C. Zeng, and Y. Tang, “An Efficient Hybrid Recommendation Model With Deep Neural Networks,” IEEE Access, vol. 7, 137900–137912, 2019.
R. Salakhutdinov and A. Mnih, “Bayesian probabilistic matrix factorization using Markov chain Monte Carlo,” in Proceedings of the 25th international conference on Machine learning - ICML ’08, Helsinki, Finland, 2008, pp. 880–887. https://doi.org/10.1145/1390156.1390267.
N. Idrissi and A. Zellou, “A systematic literature review of sparsity issues in recommender systems,” Soc. Netw. Anal. Min., vol. 10, no. 1, p. 15, 2020.
J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Syst. Appl., vol. 69, pp. 29–39, 2017.
R. Barzegar Nozari and H. Koohi, “A novel group recommender system based on members’ influence and leader impact,” Knowl. Base. Syst., vol. 205, p. 106296, 2020.
F. Deng, “Utility-based Recommender Systems Using Implicit Utility and Genetic Algorithm,” presented at the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, Shenyang, China, 2015. https://doi.org/10.2991/meic-15.2015.197.
Z. Gantner, S. Rendle, and L. Schmidt-Thieme, “Factorization models for context-/time-aware movie recommendations,” in Proceedings of the Workshop on Context-Aware Movie Recommendation – CAMRa ’10, Barcelona, Spain, 2010, pp. 14–19. https://doi.org/10.1145/1869652.1869654.
J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” p. 10.
M. de Gemmis, P. Lops, G. Semeraro, and C. Musto, “An investigation on the serendipity problem in recommender systems,” Inf. Process. Manage. vol. 51, no. 5, pp. 695–717, September 2015.
H. Ma, H. Yang, M. R. Lyu, and I. King, “SoRec: social recommendation using probabilistic matrix factorization,” in Proceeding of the 17th ACM conference on Information and knowledge mining – CIKM ’08, Napa Valley, California, USA, 2008, p. 931. https://doi.org/10.1145/1458082.1458205.
B. Lika, K. Kolomvatsos, and S. Hadjiefthymiades, “Facing the cold start problem in recommender systems,” Expert Syst. Appl., vol. 41, no. 4, pp. 2065–2073, 2014.
T. Di Noia, V. C. Ostuni, J. Rosati, P. Tomeo, and E. Di Sciascio, “An analysis of users’ propensity toward diversity in recommendations,” in Proceedings of the 8th ACM Conference on Recommender systems – RecSys ’14, Foster City, Silicon Valley, California, USA, 2014, pp. 285–288. https://doi.org/10.1145/2645710.2645774.
N. Nassar, A. Jafar, and Y. Rahhal, “A novel deep multi-criteria collaborative filtering model for recommendation system,” Knowl. Base. Syst., vol. 187, p. 104811, 2020.
Y. Shi, M. Larson, and A. Hanjalic, “Mining contextual movie similarity with matrix factorization for context-aware recommendation,” ACM Trans. Intell. Syst. Technol., vol. 4, no. 1, pp. 1–19, 2013.
Y. Koren and R. Bell, “Advances in Collaborative Filtering,” in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Boston, MA: Springer US, 2015, pp. 77–118.
T. Silveira, M. Zhang, X. Lin, Y. Liu, and S. Ma, “How good your recommender system is? A survey on evaluations in recommendation,” Int. J. Mach. Learn. Cybern. vol. 10, no. 5, pp. 813–831, 2019.
M. Srifi, A. Oussous, A. Ait Lahcen, and S. Mouline, “Recommender Systems Based on Collaborative Filtering Using Review Texts—A Survey,” Information (Basel), vol. 11, no. 6, p. 317, 2020.
R. Burke, User Model. User-adapt. Interact. vol. 12, no. 4, pp. 331–370, 2002.
M. K. Najafabadi, A. Mohamed, and C. W. Onn, “An impact of time and item influencer in collaborative filtering recommendations using graph-based model,” Inf. Process. Manage. vol. 56, no. 3, pp. 526–540, 2019.
V. Leksin and A. Ostapets, “Job recommendation based on factorization machine and topic modelling,” in Proceedings of the Recommender Systems Challenge on - RecSys Challenge ’16, Boston, Massachusetts, 2016, pp. 1–4. https://doi.org/10.1145/2987538.2987542.
P. Kouki, J. Schaffer, J. Pujara, J. O’Donovan, and L. Getoor, “Personalized explanations for hybrid recommender systems,” in Proceedings of the 24th International Conference on Intelligent User Interfaces, Marina del Ray California, 2019, pp. 379–390.
Z. Yang, P. Baraldi, and E. Zio, “Automatic Extraction of a Health Indicator from Vibrational Data by Sparse Autoencoders,” in 2018 3rd International Conference on System Reliability and Safety (ICSRS), Barcelona, Spain, Nov. 2018, pp. 328–332. https://doi.org/10.1109/ICSRS.2018.8688720.
H. Taud and J. F. Mas, “Multilayer Perceptron (MLP),” in Geomatic Approaches for Modeling Land Change Scenarios, M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, and F. Escobar, Eds. Cham: Springer International Publishing, 2018, pp. 451–455.
A. Fischer and C. Igel, “An Introduction to Restricted Boltzmann Machines,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, vol. 7441. L. Alvarez, M. Mejail, L. Gomez, and J. Jacobo, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 14–36.
J. V. Schaaf, M. Jepma, I. Visser, and H. M. Huizenga, “A hierarchical Bayesian approach to assess learning and guessing strategies in reinforcement learning,” J. Math. Psychol., vol. 93, p. 102276, 2019.
Z. Lin and H. Chen, “A Probabilistic Model for Collaborative Filtering,” in Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics – WIMS2019, Seoul, Republic of Korea, 2019, pp. 1–8. https://doi.org/10.1145/3326467.3326472.
Y. Zheng, M. Agnani, and M. Singh, “Identifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filtering,” in Proceedings of the 6th Annual Conference on Research in Information Technology – RIIT ’17, Rochester, New York, USA, 2017, pp. 1–6. https://doi.org/10.1145/3125649.3125651.
Z. Sun, L. Han, W. Huang, X. Wang, X. Zeng, M. Wang, et al., “Recommender systems based on social networks,” J. Syst. Softw., vol. 99, pp. 109–119, 2015.
A. B. Bondi, “Characteristics of scalability and their impact on performance,” in Proceedings of the second international workshop on Software and performance - WOSP ’00, Ottawa, Ontario, Canada, 2000, pp. 195–203. https://doi.org/10.1145/350391.350432.
T. Ebesu and Y. Fang, “Neural Semantic Personalized Ranking for item cold-start recommendation,” Inf. Retrieval, vol. 20, no. 2, pp. 109–131, 2017.
S. Khatwani and M. B. Chandak, “Building Personalized and Non Personalized recommendation systems,” in 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), Pune, India, 2016, pp. 623–628.
A. K. Sahu and P. Dwivedi, “Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems,” Future Gener. Comput. Syst., vol. 108, pp. 320–333, 2020.
L. Chen, G. Chen, and F. Wang, “Recommender systems based on user reviews: The state of the art,” User Model. User-adapt. Interact. vol. 25, no. 2, pp. 99–154, 2015.
Y. Gong and Q. Zhang, “Hashtag Recommendation Using Attention-Based Convolutional,” Neural Netw. vol., p. 7, 2016.
J. McAuley and J. Leskovec, “Hidden factors and hidden topics: understanding rating dimensions with review text,” in Proceedings of the 7th ACM conference on Recommender systems, Hong Kong China, 2013, pp. 165–172.
J. Y. Chin, K. Zhao, S. Joty, and G. Cong, “ANR: Aspect-based Neural Recommender,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino Italy, Oct. 2018, pp. 147–156.
Z. Cheng, Y. Ding, L. Zhu, and M. Kankanhalli, “Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web – WWW ’18, Lyon, France, 2018, pp. 639–648. https://doi.org/10.1145/3178876.3186145.
R. Zhang, H. Bao, H. Sun, Y. Wang, and X. Liu, “Recommender systems based on ranking performance optimization,” Front. Comput. Sci., vol. 10, no. 2, pp. 270–280, 2016.
A. Ortony, G. L. Clore, and A. Collins, The Cognitive Structure of Emotions, 1st ed., Cambridge University Press, 1988, https://doi.org/10.1017/CBO9780511571299.
M. Paolanti and E. Frontoni, “Multidisciplinary Pattern Recognition applications: A review,” Comput. Sci. Rev., vol. 37, p. 100276, 2020.
S. Wang, C. Aggarwal, J. Tang, and H. Liu, “Attributed Signed Network Embedding,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore Singapore, 2017, pp. 137–146.
M. Terzi, M. Rowe, M.-A. Ferrario, and J. Whittle, “Text-Based User-kNN: Measuring User Similarity Based on Text Reviews,” in User Modeling, Adaptation, and Personalization, vol. 8538, V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, and G.-J. Houben, Eds. Cham: Springer International Publishing, 2014, pp. 195–206. https://doi.org/10.1007/978-3-319-08786-3_17.
D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional Matrix Factorization for Document Context-Aware Recommendation,” in Proceedings of the 10th ACM Conference on Recommender Systems, Boston Massachusetts USA, 2016, pp. 233–240.
C. Chen, M. Zhang, Y. Liu, and S. Ma, “Neural Attentional Rating Regression with Review-level Explanations,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web – WWW ’18, Lyon, France, 2018, pp. 1583–1592. https://doi.org/10.1145/3178876.3186070.
I. Ben Sassi, S. Mellouli, and S. Ben Yahia, “Context-aware recommender systems in mobile environment: On the road of future research,” Inf. Syst., vol. 72, pp. 27–61, 2017.
S. Kulkarni and S. F. Rodd, “Context Aware Recommendation Systems: A review of the state of the art techniques,” Comput. Sci. Rev., vol. 37, p. 100255, 2020.
E. Negre, “Systèmes de recommandation contextuels?: Vers une typologie de contexte,” ROISI, vol. 1, no. 4, 2020. https://doi.org/10.21494/ISTE.OP.2020.0584.
B. Wang, S. Xiong, Y. Huang, and X. Li, “Review Rating Prediction Based on User Context and Product Context,” Appl. Sci. (Basel), vol. 8, no. 10, p. 1849, 2018.
Y. Moshfeghi, B. Piwowarski, and J. M. Jose, “Handling data sparsity in collaborative filtering using emotion and semantic based features,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information – SIGIR ’11, Beijing, China, 2011, p. 625. https://doi.org/10.1145/2009916.2010001.
J. H. Friedman, “Greedy function approximation: A gradient boosting machine,” Ann. Stat., vol. 29, no. 5, 2001. https://doi.org/10.1214/aos/1013203451.
X. Meng, S. Wang, H. Liu, and Y. Zhang, “Exploiting Emotion on Reviews for Recommender Systems,” p. 8.
A. Mogenet, T. A. N. Pham, M. Kazama, and J. Kong, “Predicting online performance of job recommender systems with offline evaluation,” in Proceedings of the 13th ACM Conference on Recommender Systems, Copenhagen Denmark, Sep. 2019, pp. 477–480.
C. Wang and D. M. Blei, “Collaborative topic modeling for recommending scientific articles,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD ’11, San Diego, California, USA, 2011, p. 448. https://doi.org/10.1145/2020408.2020480.
R.-P. Shen, H.-R. Zhang, H. Yu, and F. Min, “Sentiment based matrix factorization with reliability for recommendation,” Expert Syst. Appl., vol. 135, pp. 249–258, 2019.
K. Li, X. Zhou, F. Lin, W. Zeng, and G. Alterovitz, “Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering,” IEEE Access, vol. 7, pp. 56117–56128, 2019.
