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Awesome work, I really enjoy reading your experience on this. For the TFIDF approach, did you use clicks or purchases? I am also curious as to why you think the
by data_hans 10y ago
Awesome work, I really enjoy reading your experience on this. For the TFIDF approach, did you use clicks or purchases? I am also curious as to why you think the TFIDF approach is better in your case. I saw the imgur image that you uploaded, let's say I'm buying something for a party, isn't the buys/clicks recommendation better in that case? It'll show me all the items I may need for the party. Why would I want to see other similar items to what I'm viewing in that case?
- bartkappenburg 10y agoTF-IDF uses no click/buys data whatsoever. It's purely based on characteristics of the product (description, name, brand etc etc). Regarding the example picture I gave: that's the difference between substitutes and complementary products. The example gives alternatives (other straws...). We're building a way to have recommendations on these two types. Why TF-IDF works better vs buys? A set of bought products holds no to little similarities between those products, it's better to use it for complementary products. Why TF-IDF works better vs clicks? Click paths on sites are more random than you think. Combining click data with TF-IDF makes more sense. As said: TF-IDF as a first filter and using CF as sorting could be the most optimal for substitute products. Complementary products should rely more on CF. A problem with clicks/buys only is that new products tend to get overlooked, you can fix that by mixing in TF-IDF.