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

Recommending Best Products from E-commerce Purchase History and User Click Behavior Data

E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has purchased recently. This matrix is usually sparse and does not provide a lot of information about customer purchases or product clickstream behavior (eg., clicks, basket placement, and purchase) history, which possibly can improve product recommendations accuracy. Existing recommendation systems in E-commerce with clickstream data include those referred in this thesis as Kim05Rec, Kim11Rec, and Chen13Rec. Kim05Rec forms a decision tree on click behavior attributes such as search type and visit times, discovers the possibility of a user putting products into the basket and uses the information to enrich the user-item rating matrix. If a user clicked a product, Kim11Rec then finds the associated products for it in three stages such as click, basket and purchase, uses the lift value from these stages and calculates a score, it then uses the score to make recommendations. Chen13Rec measures the similarity of users on their category click patterns such as click sequences, click times and visit duration; it then can use the similarity to enhance the collaborative filtering algorithm. However, the similarity between click sequences in sessions can apply to the purchases to some extent, especially for sessions without purchases, this will be able to predict purchases for those session users. But the existing systems have not integrated it, or the historical purchases which shows more than whether or not a user has purchased a product before. In this thesis, we propose HPCRec (Historical Purchase with Clickstream based Recommendation System) to enrich the ratings matrix from both quantity and quality aspects. HPCRec firstly forms a normalized rating-matrix with higher quality ratings from historical purchases, then mines consequential bond between clicks and purchases with weighted frequencies where the weights are similarities between sessions, but rating quantity is better by integrating this information. The experimental results show that our approach HPCRec is more accurate than these existing methods, HPCRec is also capable of handling infrequent cases whereas the existing methods can not

Improving E-Commerce Recommendations using High Utility Sequential Patterns of Historical Purchase and Click Stream Data

Recommendation systems not only aim to recommend products that suit the taste of consumers but also generate higher revenue and increase customer loyalty for e-commerce companies (such as Amazon, Netflix). Recommendation systems can be improved if user purchase behaviour are used to improve the user-item matrix input to Collaborative Filtering (CF). This matrix is mostly sparse as in real-life, a customer would have bought only very few products from the hundreds of thousands of products in the e-commerce shelf. Thus, existing systems like Kim11Rec, HPCRec18 and HSPRec19 systems use the customer behavior information to improve the accuracy of recommendations. Kim11Rec system used behavior and navigations patterns which were not used earlier. HPCRec18 system used purchase frequency and consequential bond between click and purchased data to improve the user-item frequency matrix. The HSPRec19 system converts historic click and purchase data to sequential data and enhances the user-item frequency matrix with the sequential pattern rules mined from the sequential data for input to the CF. HSPRec19 system generates recommendations based on frequent sequential purchase patterns and does not capture whether the recommended items are also of high utility to the seller (e.g., are more profitable?).The thesis proposes a system called High Utility Sequential Pattern Recommendation System (HUSRec System), which is an extension to the HSPRec19 system that replaces frequent sequential patterns with use of high utility sequential patterns. The proposed HUSRec generates a high utility sequential database from ACM RecSys Challenge dataset using the HUSDBG (High Utility Sequential Database Generator) and HUSPM (High Utility Sequential Pattern Miner) mines the high utility sequential pattern rules which can yield high sales profits for the seller based on quantity and price of items on daily basis, as they have at least the minimum sequence utility. This improves the accuracy of the recommendations. The proposed HUSRec mines clicks sequential data using PrefixSpan algorithm to give frequent sequential rules to suggest items where no purchase has happened, decreasing the sparsity of user-item matrix, improving the user-item matrix for input to the collaborative filtering. Experimental results with mean absolute error, precision and graphs show that the proposed HUSRec system provides more accurate recommendations and higher revenue than the tested existing systems. Keywords: Data mining, Sequential pattern mining, Collaborative filtering, High utility pattern mining, E-commerce recommendation systems

A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems

E-commerce Recommendation by an Ensemble of Purchase Matrices with Sequential Patterns

In E-commerce recommendation systems, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of purchase history data will improve the accuracy of recommendations and mitigate limitations of using only explicit user ratings for recommendations. Existing E-commerce recommendation systems which have combined CF with some form of sequences from purchase history are those referred to as LiuRec09, ChioRec12, and HPCRec18. ChoiRec12 system, HOPE first derives implicit ratings from purchase frequency of users in transaction data which it uses to create user item rating matrix input to CF. Then, it computes the CFPP, the CF-based predicted preference of each target user_u on an item_i as its output from the CF process. Similarly, it derives sequential patterns from the historical purchase database from which it obtains the second output matrix of SPAPP, sequential pattern analysis predicted preference of each user for each item. The final predicted preference of each user for each item FPP is obtained by integrating these two matrices by giving 90\% to SPAPP and 10\% to CFPP so it can recommend items with highest ratings to users. A limitation of HOPE system is that in user item matrix of CF, it does not distinguish between purchase frequency and ratings used for CF. Also in SPM, it recommends items, regardless of whether user has purchased that item before or not. This thesis proposes an E-commerce recommendation system, SEERs (Stacking Ensemble E-commerce Recommendation system), which improves on HOPE system to make better recommendations in the following two ways: i) Learning the best minimum support for SPA, best k similar users for CF and the best weights for integrating the four used matrices. ii) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF not purchased, SPM purchased, SPM not purchased and purchase history matrix which are obtained and merged with the better-learned parameters from (i) above. Experimental results show that by using best weights discovered in training phase, and also separating purchased and not purchased items in CF and sequential pattern mining methods, SEERS provides better precision, recall, F1 score, and accuracy compared to tested systems

Discovering E-commerce Sequential Data Sets and Sequential Patterns for Recommendation

In E-commerce recommendation system accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that use mining techniques with some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. LiuRec09 system clusters users with similar clickstream sequence data, then uses association rule mining and segmentation based collaborative filtering to select Top-N neighbors from the cluster to which a target user belongs. ChoiRec12 derives a user’s rating for an item as the percentage of the user’s total number of purchases the user’s item purchase constitutes. SuChenRec15 system is based on clickstream sequence similarity using frequency of purchases of items, duration of time spent and clickstream path. HPCRec18 used historical item purchase frequency, consequential bond between clicks and purchases of items to enrich the user-item matrix qualitatively and quantitatively. None of these systems integrates sequential patterns of customer clicks or purchases to capture more complex sequential purchase behavior. This thesis proposes an algorithm called HSPRec (Historical Sequential Pattern Recommendation System), which first generates an E-Commerce sequential database from historical purchase data using another new algorithm SHOD (Sequential Historical Periodic Database Generation). Then, thesis mines frequent sequential purchase patterns before using these mined sequential patterns with consequential bonds between clicks and purchases to (i) improve the user-item matrix quantitatively, (ii) used historical purchase frequencies to further enrich ratings qualitatively. Thirdly, the improved matrix is used as input to collaborative filtering algorithm for better recommendations. Experimental results with mean absolute error, precision and recall show that the proposed sequential pattern mining-based recommendation system, HSPRec provides more accurate recommendations than the tested existing systems