PURCHASING AND SELLING GOODS RECOMMENDER SYSTEM USING PROGRESSIVE MINING OF SEQUENTIAL PATTERNS (PISA) WITH MULTI CONSTRAINT
Bukalapak recommender system has been transformed from Content-Based which uses the similarity between name and item’s description to the similarity between other users’ preferences. However, the recommender system should have attracted users’ preferences holistically. Moreover, there are several...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/51143 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Bukalapak recommender system has been transformed from Content-Based which uses the
similarity between name and item’s description to the similarity between other users’
preferences. However, the recommender system should have attracted users’ preferences
holistically. Moreover, there are several special characteristics of ecommerce transactions: have an order by time, have purchasing patterns for every user,
and have stakeholders' unique paradigm to simplify decision making. From these three
characteristics, PISA algorithm with multi constraint will be used because it gives time
domain in form of timestamp and adds constraint as special criteria for the stakeholder.
Furthermore, this algorithm eliminates obsolete transactions to give space for new
transactions in form of sliding window POI which is determined by the stakeholder. This
algorithm then becomes the preprocess for recommender system using users’ purchase
history. This algorithm is trained with 9982 synthetic transactions from Bukalapak with 40
items and 49 users and then processed to be recommendations for users with a website as a
medium. The experiment result shows that only 0 or 1 item is recommended with algorithm
PISA with multi constraint as preprocess because the synthetic transactions are too few.
This means more transactions are needed to give more recommendations.
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