Abstract
In recent years, Artificial Intelligence (AI) techniques like (a) fuzzy sets, (b) Artificial Neural Networks (ANNs), (c) Artificial Immune Systems (AIS) (d) Swarm Intelligence (SI), and (e) Evolutionary Computing (EC) are used to improve recommendation accuracy as well as mitigate the current challenges like Scalability, Sparsity, Cold-start etc. Aim of the survey is to incorporate the recommender system in light of the AI techniques. Various AI techniques are presented and recommender system’s challenges are also presented. Moreover, we have tried to study the ability of AI techniques to deal with the above mentioned challenges while designing recommender systems. Furthermore, pros and cons of AI techniques are discussed in detail.
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Gabrani, G., Sabharwal, S., Singh, V.K. (2017). Artificial Intelligence Based Recommender Systems: A Survey. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_6
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DOI: https://doi.org/10.1007/978-981-10-5427-3_6
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