Abstract
The importance of recommender system is largely increasing in many E-commerce platforms. When we try to create an algorithm to complete the recommendation tasks, we should not only consider their performance on recommending all items, but also their performance on recommending niche items because of long tail effect. The successful cases of long tail effect exist in Internet. Amazon and eBay all have very success applications in this aspect. In this paper, we specifically concentrate on the factors which can influence the performance of algorithms on recommending all items and niche items. By strengthening the factors, we can enhance the ability of recommender algorithms to recommend all items or niche items to meet different needs. At the same time, these factors can provide a new view to explain the algorithms. The algorithms which we selected include collaborative filtering algorithms, the graph-based model and the content-based model. We compare the algorithms commented before on Top-N recommendation tasks and evaluate their performance on recommending long-tail items. Experimental results support the effect of factor analysis for various tasks.
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Acknowledgements
The work is funded by the Shanghai Undergraduate Student Innovation Project, the National Natural Science Foundation of China (No. 61170155) and the Shanghai Innovation Action Plan Project (No. 16511101200).
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Xiao, Z., Zhang, Y., Fang, Y. (2019). Exploring Influential Factors in Recommending Algorithms for All Items and Niche Items. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_18
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DOI: https://doi.org/10.1007/978-3-319-98776-7_18
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