# A Survey of Sequential Pattern Based E-Commerce Recommendation Systems
## 3 Taxonomy for Sequential Pattern-Based E-Commerce Recommendation Systems
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**Rec Sys Performance** & **Reducing Data Sparsity** & **Improving Novelty** & **Improving UCI-Skalability** & **Improving UCI-S Rating Quality** & **Improving UCI-S Rating Quantity** \\ \hline ChoRec05 & Low & High & Medium & Low & Low \\ \hline ChenRec09 & Medium & Low & Low & Medium & Medium \\ \hline HuangRec09 & Low & High & Medium & Low & Low \\ \hline LiuRec09 & Low & High & Medium & Low & Low \\ \hline ChoiRec12 & Medium & Low & Low & Medium & Medium \\ \hline Hybrid Model RecSys16 & Medium & Low & Medium & Medium & Medium \\ \hline Product RecSys16 & Medium & High & High & Medium & Medium \\ \hline SainiRec17 & Medium & Low & Low & Medium & Medium \\ \hline HPCRec18 & High & Low & Low & High & High \\ \hline HSPRec19 & High & High & Medium & High & High \\ \hline \hline \end{tabular}
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Table 40: Effect of SP on the performance of the surveyed recommendation systems. Improving user-item (U-I) rating quality and quantity  
Low: no historical user purchases, clickstream behavior, frequency of purchases, or other user purchase behavior is mined for integration into the U-I rating matrix Medium: minimum information, i.e., only one among user purchase behaviors such as association rules (which are not as informative as sequential patterns), historical user purchases, clickstream behavior, and frequency of purchase are incorporated into the U-I rating matrix, resulting in a less complex method of mining user purchase behavior High: more informative historical customer purchase behavior features are mined and incorporated into the U-I rating matrix, such as clickstream behavior, consequential bond information on the historical clicks and purchases of a user, historical sequential purchase behavior (sequential patterns), etc. Early hybrid recommendation systems such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, and ChoiRec12 use association rule mining to improve the quality of rating input. None of these systems incorporates additional customer behavioral data, such as clickstream data or browsing history, from which implicit behavior can be extracted and used to fill in the unknown ratings. Hence, these systems are assigned a \"low\" level in terms of reducing data sparsity. The HSPRec19 system can achieve this to a higher extent by using SPM (GSP algorithm) to derive sequential patterns, thereby improving both the rating quality and quantity. Thus, this system is assigned a \"high\" level with respect to reducing data sparsity. The remaining four systems (Hybrid Model RecSys16 [40], Product RecSys16 [40], SainRec17 [42], and HPCRec18 [43]) do not integrate any additional behavior, instead extracting sequential patterns using SPM algorithms such as PrefixSpan [33], FreeSpan [48], and SPADE [32], which results in reducing data sparsity to a \"medium\" level. The novelty rate is defined \"low\" if the items previously purchased by the target user are included in the recommendation list, as novelty accounts for the likelihood of a recommendation system providing recommendations to the user that they are not yet aware of. Thus, the novelty rate is defined \"high\" if known items are excluded from recommendation, while products associated with the purchased products instead used for recommendation purposes to make the customer aware of potentially related products. The dimensionality of a dataset can be reduced using either a clustering technique or by explicitly using a dimensionality reduction technique, and sometimes both. Downsizing the data dimension leads to an increase in the scalability of the recommendation system.