ProductRec: Product Bundle Recommendation Based on User's Sequential Patterns in Social Networking Service Environment

• Published in: 2017 IEEE International Conference on Web Services (ICWS) pp. 301 - 308
• Main Authors: Wenli Yu, Li Li, Xiaofei Xu, Dengbao Wang, Jingyuan Wang, Shiping Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2017
• Subjects: Collaboration; Data models; Feature extraction; Latent Bundle Relationships; Markov processes; Predictive models; Recommender systems; Semantics; Sequential Patterns
• DOI: 10.1109/ICWS.2017.127

With the overload of information on the Web, Recommender Systems (RSs) are becoming increasingly popular and have been employed to provide suggestions to meet different requirements. RSs are utilized in a variety of areas including movies, music, social tags, user group and products as Web services evoked on the Internet either as mobile Apps or PC-based applications. However, it is challenging to achieve personalized recommendations instead of offering up too many lowest common denominator recommendations. Understanding how products relate to each other is important because it has great impact on the performance. Furthermore, the personalized sequential behavior, which is closely related to a particular product, is essential for recommender systems. Most models simply integrate features from users and items without considering potential product bundle relationships between products exposed by users' personalized sequential behaviors. In this paper, a novel method based on Factorizing Personalized Markov Chain (FPMC) is proposed to comprehensively explore the latent bundle relations from users perspective, along with the hidden correlative semantics between products obtained from logic regression method, which provides a unified view to describe the user preferences, product/item features, and the user sequential patterns in timely manner. The involved semantic features are extracted using deep learning models. We evaluate our method on real-world Amazon datasets and our framework significantly outperforms other baseline models, especially on sparse datasets. The experimental results show that our approach qualitatively captures personalized behaviors with superior recommendation performance.