Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network

Consumer behaviors (e.g., clicking products, adding products to favorites, adding products to carts, and purchasing products) play important roles in inferring consumers’ interests for product recommendation. Although studies have been conducted to incorporate the consumer behaviors for product recommendation, the heterogeneity of the behaviors and their composites were seldom explored for product recommendation. There is a need to capture the heterogeneity of the consumer behaviors and reveal their importance in the product recommendation because the behaviors indicate different consumer preferences for products. To bridge the gap, this research proposes a heterogeneous network-based approach to leverage the consumer behaviors for product recommendation. The proposed approach represents consumers and products as different types of nodes and behaviors as different types of edges. Meta paths that describe behavioral relationships between the consumers and products are used to calculate their similarities, which are further used to generate recommendations. To select informative meta paths for product recommendation, a heuristic selection mechanism is proposed. Besides, the research uses a non-negative matrix factorization method to learn the weights of the selected meta paths and then makes personalized recommendations for consumers. Experimental results based on real-world data demonstrate that the proposed approach not only helps to understand the roles of different consumer behaviors in product recommendation, but also achieves better recommendation performance than several baseline methods.