A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Abstract

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

Uncovering Insights for New Car Recommendations with Sequence Pattern Mining on Mobile Applications

“…In recent years, researchers have explored combining multiple methods to improve the effectiveness and efficiency of sequence pattern mining. One example is the SPAP method (Sequential rule, Periodic pattern, Association rule, and Preference), which incorporates association rules, sequential analysis, and periodic patterns to capture complex customer purchase behavior and improve personalized recommendations [5]. Another recent study applied sequential pattern mining to investigate airport ground handling operations [7].…”

“…SPAP [5] SPAP combines association rules, sequential analysis, and periodic patterns to capture complex customer purchase behavior.…”

“…Additionally, future research can combine different research methods, such as sequence pattern mining and text mining [17][18][19], to construct consumer preference trends by integrating sequence rules, product attributes, and textual data. By integrating new methods [5,[20][21][22][23] into the sequence pattern extraction process, the accuracy and effectiveness of product recommendations can be further improved.…”

“…Sequential pattern mining has been suggested as a promising alternative to address this limitation. However, while several studies have improved the efficiency at which sequence rules can be generated [5][6][7][8][9][10][11][12], few researchers have made their software code public [4], and only a few have been carried out on mobile app data, posing a challenge for small app vendors with limited financial resources who depend on ads to provide free services to their users.…”

PowerMat: context-aware recommender system without user item rating values that solves the cold-start problem

“…One such sub-field is context-aware recommendation -the field that combines contextual information together with user item rating values to predict the preferences of users over a set of items. A workshop named CARS [1][2] [3] has been held as an affiliating research venue with RecSys since early 2010's on the topic, but the number of accepted papers remained very small in recent years. In the industry, corporations such as Burger King and Ctrip have been using the technology to increase their traffic and sales volumes.…”