# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 4 Conclusions and Future Work  
Recommendation Systems open up new opportunities to retrieve personalized information on the internet, enabling users to have access to products and services which are not readily available to users of the system. Many recommendation systems neglect sequential patterns during recommendation. Thus, to verify the necessity of sequential patterns in E-commerce recommendation systems, in this review we have conducted a survey of existing SP-based E-commerce recommendation systems. In addition, we have developed a taxonomy that classifies these applications by their recommendation method as well as by performance factors such as reducing data sparsity, improving the scalability of recommendation systems, and improving the accuracy and novelty of recommendations. After performing a comparative analysis of traditional CF against few of the surveyed SP-based E-commerce recommendation systems, our results prove that the hybridization of SPM with CF by integrating sequential patterns into the user-item rating matrix input can improve recommendation quality in terms of accuracy, diversity, and novelty. Additionally, we would direct interested readers to several open research subjects that warrant future work in the area of SP-based E-commerce recommendation systems. Ideas for future work in this direction include the following aspects:None of the reviewed studies has exactly measured the level of probability of purchase determined by each SP; instead, the general mid-point of 50% [6] is used as an example. Hence, more information in historical data (such as the frequency of patterns occurring together) should be used to determine the exact level of purchase probability (e.g., 0.5 to 1.0) for each SP. 2. Additional ways of incorporating clickstream sequences/patterns into the user-item rating matrix should be found, with the use of consequential bonds used to improve the quality of the input user-item rating. Moreover, additional information such as contextual data (e.g., the time of year, such as the season, month, or day of the week) should be integrated into user-item preferences. 3. Incorporating a profit or utility factor to find new patterns other than the frequent sequential patterns in historical purchase data could result in more profitable recommendations. Thus, sequential patterns with high utility should be integrated into the process of generating recommendations. 4. In real-world scenarios, items purchased by a user during a certain time period are often from multiple domains, rather than only one domain. There are many sequential dependencies between items from different domains, e.g., the purchase of car insurance after the purchase of a car. Such cross-domain sequential dependencies are ignored in most sequential pattern-based recommendation systems. Therefore, cross-domain recommendation systems represent another promising research direction that could generate more accurate recommendations by leveraging information and diverse recommendations from different domains. 5. Another promising line for future research is the evaluation strategy used to assess the performance of sequential pattern-based recommendation systems, as all the reviewed studies were evaluated based on offline approaches. Although offline evaluation has lower cost, and there is no risk of bias due to active user involvement, as in the case of online and user studies, the results mostly contradict when applied to real-life scenarios involving online and user study evaluations. Therefore, there is a need for more research on evaluation strategies to compare performance based on different measures other than accuracy and offline evaluation, such as real-time evaluations, as well as the novelty, coverage, serendipity, and diversity of recommendations. This paper was written by [PERSON]. (first author) from an unpublished MSc thesis of her graduate student; the second author, H.K., reviewed and read initial versions of the paper. All authors have read and agreed to the published version of the manuscript.