Abstract
This study focuses on diversifying recommendations and improving recommendation accuracy by incorporating multi-category purchase interval and price in a novel way. We suggest a method to model the drift of a user’s interest for different categories based on sequential pattern mining. Additionally, we propose a new approach to model personalized multi-category inter-purchase interval for the user. By employing fuzzy set theory, we also put forward an approach to model the price preferences for each user. We propose a recommender system that incorporates the methods mentioned above. The experimental results based on real purchase records show that the proposed recommendation algorithm has high stability and superior performance in recommending products with different characteristics. The results also demonstrate the effectiveness of incorporating purchase interval and price factor in recommender systems. This study demonstrates the existence of particular inter-purchase intervals among different categories, and indicates that price is an important factor influencing customers’ decision making.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
