Title: A personalised recommendation algorithm based on probabilistic neural networks

Authors: Long Pan; Jiwei Qin; Liejun Wang

Addresses: School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China ' Network and Information Technology Center, Xinjiang University, Urumqi 830046, China ' School of Software, Xinjiang University, Urumqi 830046, China

Abstract: Collaborative filtering is widely used in recommendation system. Our work is motivated by the observation that users caught in their attention relationship network, and their opinions about items will be directly or indirectly affected by others through such a network. Based on behaviours of users with similar interest, the technique focuses on the use of their opinions to recommend items. Therefore, the quality of similarity measure between users or items has a great impact on the accuracy of recommendation. This paper proposes a new recommendation algorithm with graph-based model. The similarity between two users (or two items) is computed from the connections on graph with nodes of users and items. The computed similarity measure is based on probabilistic neural networks to generate predictions. The model is evaluated on a recommendation task which suggests that which videos users should watch based on what they watched in the past. Our experimental results on the YouKu and Epinions datasets demonstrate the effectiveness of the presented approach in comparison with both collaborative filtering with traditional similarity measures and simplex graph-based methods and further improve user satisfaction, our approach can better improve the overall recommendation performance in precision, recall and coverage.

Keywords: recommendation system; similarities; graphs-based approach; collaborative filtering; probabilistic neural networks; PNN.

DOI: 10.1504/IJICT.2019.101860

International Journal of Information and Communication Technology, 2019 Vol.14 No.4, pp.385 - 402

Received: 01 Sep 2017
Accepted: 20 Oct 2017

Published online: 23 Aug 2019 *

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