901 research outputs found
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
POLICY OF NATIONALISM GUIDANCE THROUGH IN TRADITIONAL MARKET MANAGEMENT IN CENTRAL JAVA
A research on policy nationalism guidance through in traditional markets management in the province of Central Java is implemented in “Pasar Gede Solo” with qualitative methods. The reason for selecting “Pasar Gede Solo” because of Solo City has a lot of cultural heritages that are still held strong until today. The cultural heritage is the local identity. The Local identity can develop into the province identity, then to be the national identity. A strong national identity shows high Nationalism which reflected from loyalty, passion and pride of the nation itself. The number of local identities in “Pasar Gede Solo” is likely to evolve into national identity should be encouraged to preserve the Government's policy to strengthen Indonesia Nationalism
Multi-Relational Contrastive Learning for Recommendation
Personalized recommender systems play a crucial role in capturing users'
evolving preferences over time to provide accurate and effective
recommendations on various online platforms. However, many recommendation
models rely on a single type of behavior learning, which limits their ability
to represent the complex relationships between users and items in real-life
scenarios. In such situations, users interact with items in multiple ways,
including clicking, tagging as favorite, reviewing, and purchasing. To address
this issue, we propose the Relation-aware Contrastive Learning (RCL) framework,
which effectively models dynamic interaction heterogeneity. The RCL model
incorporates a multi-relational graph encoder that captures short-term
preference heterogeneity while preserving the dedicated relation semantics for
different types of user-item interactions. Moreover, we design a dynamic
cross-relational memory network that enables the RCL model to capture users'
long-term multi-behavior preferences and the underlying evolving cross-type
behavior dependencies over time. To obtain robust and informative user
representations with both commonality and diversity across multi-behavior
interactions, we introduce a multi-relational contrastive learning paradigm
with heterogeneous short- and long-term interest modeling. Our extensive
experimental studies on several real-world datasets demonstrate the superiority
of the RCL recommender system over various state-of-the-art baselines in terms
of recommendation accuracy and effectiveness.Comment: This paper has been published as a full paper at RecSys 202
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