A Novel Customer-Oriented Recommendation System for Paid Knowledge Products

Abstract

With the rapid development of knowledge payment, customers are faced with a large number of knowledge products when purchasing, leading to the need for an effective recommendation system. However, existing recommendation systems cannot accurately and adequately represent paid knowledge products with implicit but specialized features and sparse interactive histories, and thus are deemed not suitable for such products. In this paper, we propose a novel recommendation system for knowledge products, the core of which is the designed customer-oriented representation of knowledge products. Specifically, we utilize customer activity information on the free knowledge sharing platform as the knowledge document for each customer of paid knowledge products, to extract customer knowledge background and preference. Then, a deep learning-based model Doc2vec is adopted to transfer knowledge documents to customer knowledge background vectors. Such vectors of a particular paid knowledge product are further aggregated to a product-level vector for customer-oriented product representation, based on which two recommendation results are generated with product ratings and similarities of paid knowledge products, respectively. Extensive comparative experiments are conducted to demonstrate the effectiveness of the proposed system for the representation and recommendation of paid knowledge products. This paper will contribute to the literature of knowledge payment and recommendation systems, as well as provide practical implications for the information service and the operation of knowledge products on knowledge payment platforms.

Keywords

Recommendation system / knowledge product / knowledge payment / Doc2vec / product representation

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