Category: New Price2Spy features
It is a well-known fact that some sites do their best to prevent automated price monitoring performed by Price2Spy and similar tools. Ocado.com is one of these sites, putting in a lot of effort in banning such traffic. After we have exhausted all classic ban-avoiding techniques, we have resorted to our strongest weapon – Stealth IP...
Sometimes, you just know when it’s time for a change. Today, we’re proud to announce the launch of our revamped Price2Spy 2.0 app! We’ve redesigned our app with you in mind.   Guided by the saying that a suit does not make a man, but it says a lot about him, we decided to tackle the...
Product matching in Price2Spy Previous topic: (Part #9) ML does work, but it’s not magic Overall results did vary from one industry to another, from one client to another – is also very much depended on factors N (number of matching candidates) and M (the ease of match approval vs establishing a match fully manually)...
Product matching in Price2Spy Previous topic: (Part #8) Product matching via ML: Testing on various industries/languages Next topic: (Part #10) Product matching via ML: The results Since we said that we cannot afford to have wrong matches in Price2Spy, we were particularly careful when testing ‘false positives’ – these were matches where ML scored a...
Product matching in Price2Spy Previous topic: (Part #7) Product matching via ML: Post-processing Next topic: (Part #9) ML does work, but it’s not magic The Product Matching project had been going on for a while, now was the time to remember our initial decisions, and put them to the test: Languages – we have too...
Product matching in Price2Spy Previous topic: (Part #6) Evaluating ML training results Next topic: (Part #8) Testing on various industries/languages This part was logically the most difficult to comprehend, at least for me. Let’s give it a try: We have 50K products from Set A and 40K products from Set B. We have already eliminated...
Product matching in Price2Spy Previous topic:  (Part #5) ML training Implementation Next topic: (Part #7) Post-processing How do you know if your ML model works fine? The answer is seemingly simple: you run all of the potential matching combinations through Random Forest (RF), in order to get a matching score of each combination. Then, if...
Product matching in Price2Spy Previous topic: (Part #4) Preparing the ML training set Next topic: (Part #6) Evaluating ML training results Our very first implementation step was a bit non-standard. While most of ML is done in Python, Price2Spy is a Java shop. We respect other technologies, but our love and our choice go to...
Product matching in Price2Spy Previous topic: (Part #3) For ML experts – why is product matching so difficult? Next topic: (Part #5) ML training Implementation This part was particularly tough. When performing the product matching, one would need to match Set A (Products from Website A) to Set B (Products from Website B). However, in...
Product matching in Price2Spy Previous topic: (Part #2) Product matching via Machine Learning – Important decisions to be made Next topic: (Part #4) Preparing the ML training set This chapter is of technical nature, and it explains the difficulties Price2Spy’s team had to overcome when building the ML model for product matching. 1. Computation size...
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About Price2Spy Blog

This blog is a place for eCommerce professionals to discuss ideas, methodologies and strategies to compete more effectively in the ever more tightening world of online retail. We explore things like competitive price monitoring, competitor business intelligence, competitive pricing, and counter-intelligence in general.

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