Next Basket Recommendation - Introduction

Introduction

Recommender systems have become increasingly popular for organizations aiming to enhance product promotion and sales using advanced tools and algorithms. One specific area within this field is Next Basket Recommendation (NBR), which focuses on predicting the next set of items a user is likely to purchase or consume. This application is particularly prevalent in e-commerce and retail industries. Two notable methods in this domain are discussed:

Personal Top Frequency (TopPersonal)

Personal Top Frequency (TopPersonal) is a straightforward approach in Next Basket Recommendation (NBR) that leverages the frequency of items in a user’s historical baskets to predict their next basket. Here’s how it works:

Methodology: TopPersonal directly selects the most frequently occurring items from a user’s past purchase history and presents them as the predicted items for the next basket.
Advantages: It serves as a strong baseline due to its simplicity and effectiveness across various datasets and evaluation metrics. By focusing on frequent items, it typically performs well in recommending items that users have previously shown interest in.
Limitations: TopPersonal only considers items that a user has already purchased in the past. This means it may overlook new or unseen items that the user might be interested in, especially if their preferences change over time or if they haven’t purchased certain items recently.

TIFU-KNN (Temporal-Item-Frequency-Based User-KNN)

TIFU-KNN introduces a more sophisticated approach to NBR by integrating temporal dynamics and collaborative filtering techniques. Here’s a detailed breakdown:

Temporal Dynamics: Unlike TopPersonal, TIFU-KNN takes into account the temporal aspect of item purchases. It assigns weights to items based on when they were last purchased, with more recent purchases carrying higher significance.
User Similarity Calculation: TIFU-KNN calculates user similarity using historical records of item purchases. It aggregates user behavior into a vector representation, adjusting for temporal changes in preferences. Users who have similar historical purchase patterns are considered more alike.
Prediction Mechanism: In the prediction phase, TIFU-KNN combines two main components:
- Repeated Purchase Component (ut): This represents the user’s historical purchase behavior, emphasizing items they have frequently bought in the past.
- Collaborative Purchase Component (un): This is derived from the average behavior of the user’s nearest neighbors in a k-nearest neighbors (KNN) approach. It captures collaborative patterns among users with similar preferences.
Linear Combination: TIFU-KNN combines these components using a linear combination weighted by a hyper-parameter α:
$P = \alpha \cdot u_t + (1-\alpha) \cdot u_n$
Here, P represents the predicted next basket, where ut is the user’s own purchase vector and un is the average vector of their nearest neighbors.
Recommendation: Based on this prediction P, TIFU-KNN recommends the top s items with the highest scores as the next-basket recommendations for the user.

Conclusion

While TopPersonal excels in simplicity and baseline performance using frequent itemsets, TIFU-KNN advances NBR by integrating temporal dynamics and collaborative filtering. It addresses the limitations of TopPersonal by adapting to changing user preferences over time and leveraging collaborative patterns among users. This makes TIFU-KNN particularly effective in dynamic environments where user preferences evolve and new item introductions are common.