Abstract
In order to increase sales for e-commerce websites and meet customer expectations, recommender systems need to recommend more niche products consumers might like. However, traditional product recommender systems usually aim to improve the recommendation accuracy while overlook the diversity within the recommendation lists. In this paper, firstly we examine the importance of diversity within recommended lists through a psychological survey. Motivated by our observations, we develop a general framework, called DivRec, to improve recommendation diversity without lowering accuracy. Experimental results on an e-commerce dataset demonstrate that our approach outperforms state-of-the-art techniques in terms of both accuracy and diversity.
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He, K., Niu, J., Sha, C. (2014). DivRec: A Framework for Top-N Recommendation with Diversification in E-commerce. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_34
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DOI: https://doi.org/10.1007/978-3-319-11116-2_34
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