Improving Online Shopping Recommendations with Timing

Next basket recommendation is a significant topic in online shopping. It helps predict what customers might buy next based on their past purchases. This recommendation system is essential for many e-commerce platforms. As online shopping grows, finding ways to make these Recommendations more precise is crucial.

Traditionally, many methods have been used to improve next basket recommendations. However, a common issue is that they often ignore the Timing of purchases. Time can affect what customers want to buy next. This article introduces a new method that includes timing to improve recommendations.

#The Need for Better Recommendations

Customers tend to buy similar items repeatedly. For example, if someone buys milk, they are likely to buy it again soon. However, some items, like cleaning products, may not be purchased as frequently. This inconsistency in buying patterns makes it challenging for systems to recommend items accurately.

Current methods often fail to consider the timing of past purchases. When a customer makes a purchase, the time gap until their next purchase can influence their buying habits. By not considering this time gap, systems can provide recommendations that may not fit the customer's current needs.

#The Proposed Method

To address these challenges, a new approach called Time-Aware Item Weighting (TAIW) has been developed. This method takes into account both the timing of purchases and the items' past purchase frequency.

#How TAIW Works

TAIW consists of two main parts: the Repurchase Module and the Neighbourhood Module.

#Repurchase Module

This part focuses on understanding when customers are likely to repurchase items. It studies how long it typically takes for customers to buy the same item again. The module assigns scores to items based on their relevance at the current time. For instance, if a customer just bought soap, the module recognizes that soap is still relevant and may suggest it again.

The scoring process also considers different buying patterns for various items. Some items, like food, might have a short repurchase cycle, while others, like electronics, might take longer.

#Neighbourhood Module

This part looks at similar customers. By analyzing what other users with similar shopping habits have purchased, the system can improve the recommendations. For example, if many customers who bought bread also bought butter, the system might suggest butter to a customer who frequently buys bread.

#Importance of Timing

Timing is crucial in the recommendation process. If a long time has passed since the last purchase, the preferences of the customer might have changed. TAIW adjusts its recommendations based on how long it has been since the customer's last basket. This way, it can provide more relevant suggestions.

For example, if a customer bought a summer dress last year, the system recognizes that it may not be relevant now. With TAIW, the system can adapt to these changes and offer more meaningful recommendations.

#Experiments and Results

To see how well TAIW performs, experiments were conducted using real-world shopping data. The results revealed that TAIW significantly outperformed many existing recommendation methods.

#Comparison with Other Methods

In tests, TAIW showed improvements of 3% to 8% compared to traditional systems. This indicates that the inclusion of timing in recommendations offers a tangible benefit. When looking at different recommendation sizes, TAIW consistently performed better than its competitors.

#Impact of Time Gaps

The experiments also looked at how well the system performs when there is a significant time gap between the last purchase and the recommendation. TAIW showed resilience, maintaining good performance even when this gap was large. This contrasts with other methods, which struggled to make accurate recommendations over longer periods.

#Analysis of Components

Further analysis focused on the individual parts of TAIW to see how they contributed to the overall performance. It was found that both the Repurchase Module and the Neighbourhood Module played vital roles. Removing either module led to a decrease in recommendation quality, confirming their importance in the overall system.

#Conclusion

In summary, TAIW offers a fresh approach to next basket recommendations by incorporating timing into the process. By recognizing the importance of when items are purchased and considering user similarities, TAIW enhances the overall recommendation experience.

The method addresses key issues seen in previous approaches, making it a valuable tool for e-commerce platforms. The promising results from experiments indicate that TAIW can significantly improve how users are recommended products. As online shopping continues to evolve, methods like TAIW will become essential for creating personalized experiences that drive customer satisfaction and loyalty.