Cross-domain novelty seeking trait mining for sequential recommendation

TL;DR

This paper introduces a novel cross-domain model, CDNST, that leverages transfer learning to better capture users' novelty-seeking traits in sequential recommendation systems, addressing data sparsity issues.

Contribution

The paper proposes the first cross-domain model for mining novelty-seeking traits in sequential recommendation, incorporating temporal data analysis to enhance recommendation accuracy.

Findings

CDNST outperforms baseline models on three Douban datasets.

Temporal properties significantly influence model performance.

Simulation experiments validate the importance of temporal analysis.

Abstract

Transfer learning has attracted a large amount of interest and research in last decades, and some efforts have been made to build more precise recommendation systems. Most previous transfer recommendation systems assume that the target domain shares the same/similar rating patterns with the auxiliary source domain, which is used to improve the recommendation performance. However, to the best of our knowledge, almost these works do not consider the characteristics of sequential data. In this paper, we study the new cross-domain recommendation scenario for mining novelty-seeking trait. Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. Previous work performing on only one single target domain may not fully characterize users' novelty-seeking trait well due to the data scarcity and sparsity, leading to the poor recommendation performance. Along this line, we proposed a new cross-domain novelty-seeking trait mining model (CDNST for short) to improve the sequential recommendation performance by transferring the knowledge from auxiliary source domain. We conduct systematic experiments on three domain data sets crawled from Douban (www.douban.com) to demonstrate the effectiveness of the proposed model. Moreover, we analyze how the temporal property of sequential data affects the performance of CDNST, and conduct simulation experiments to validate our analysis.