Incorporating frequency, recency and profit in sequential pattern based recommender systems

Customers usually change their purchase interests in the short product life cycle of the e-commerce environment. Therefore, recent transaction patterns should have a greater effect on the customer preferences. From the seller's point of view, an e-commerce recommender system should focus on the profit of recommendation. This study proposes a new sequential pattern mining algorithm that incorporates the concepts of frequency, recency, and profit to discover frequent, recent, and profitable sequential patterns, called FRP-sequences. Based on the discovered sequential patterns, this study develops a collaborative recommender system to improve recommendation accuracy for customers and the profit of recommendation from the seller's perspective. The proposed recommender system clusters customers, discovers FRP-sequences for each cluster, and then recommends items to the target customers based on their frequent, recent, and profitable FRP-sequences. In the stage of discovering FRP-sequences, the transaction patterns near the current time period and profitable items are weighted more heavily to improve profit. This study uses a public food mart database to determine the performance of the proposed approach, and compares it with traditional recommendation models. The proposed system performs better than traditional recommendation models in both recommendation accuracy and profit.