A Hybrid Approach to Three-Way Conversational Recommendation

Conversational recommendation is ubiquitous in e-commerce, while three-way recommendation provides friendly choices for service providers and users. However, their combination has not been studied yet. In this paper, we introduce the three-way conversational recommendation problem, and design the hybrid conversational recommendation (HTCR) algorithm to address it. First, a new recommendation problem is defined by considering the man-machine interaction as well as the misclassification and promotion costs. The optimization objective of the problem is to minimize the total cost. Second, a popularity-based technique is designed for user cold-start recommendation, where the user maturity is responsible for deciding when HTCR turns to the second technique. Third, an incremental matrix factorization technique is designed for regular recommendation. It is efficient since only a few rounds of training are needed for newly acquired user feedback. Experiments were undertaken on three well-known datasets, including Jester, MovieLens 100K, and MovieLens 1M. Results demonstrated that our algorithm outperformed state-of-the-art ones in terms of average cost.