3 ms·

I liked the way the article compared the two approaches (CF and text analysis) and the code was interesting. At Conversify[0] we literally tested these methods

I liked the way the article compared the two approaches (CF and text analysis) and the code was interesting. At Conversify[0] we literally tested these methods last week(!) We help e-commerce companies with machine-learning optimizations of which a recommendation system is one. The average shop uses the standard tools for recommendations which are terrible, with some intelligence it could be a very important way to increase sales and conversion. [/end-plug] This is what we learned. We've tested three approaches (all on sites with enough traffic): (1) CF based on buys in combination with/without profiling (2) CF based on purely clicks in combination with/without profiling (3) Text analysis (sklearn, TF-IDF) We had the best hopes for buys then clicks and as last text-analysis. It was exactly the other way around ;-). (1) Buys has the drawback that the relationship of profiles to products is a lot to a few most of the time (relatively). (2) Clicks has the same drawback but we can also see clicks within one session. This makes clicks somewhat better because shoppers often compare multiple products that are alike. This is valuable info. (3) We didn't think that TF-IDF had evolved the last couple of years this much that we now can do an analysis with a few a lines of code with these results. They were stellar. We tested the outcomes with the business owners as well as customers (we didn't say which method generated which results) and they all went massively for text-analysis. Here's our outcome for a random product from one of our customers (with a combined buys/clicks recommendation): http://i.imgur.com/lEYYIh9.png http://i.imgur.com/lEYYIh9.png That being said: we are working on a combination of all three methods. We think that text-analysis is for the first filtering of related products and that the CF methods are for ordering of this set. Also using weights for the different methods are useful. Besides that we are developing a way to distinguish in an automated way between substitute products (samsung vs iphone) and complementary products (charger with a phone). This could be accomplished by using more CF than text (and specifically bought products within sessions). Our first results are promising. [0] https://www.conversify.com https://www.conversify.com

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 ago
  TF-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.
numlocked 10y ago
(article author here) Very interesting result. What techniques were you using for CF? You're right that buys:profiles ratio is a challenge, but dealing with a very sparse matrix like that is exactly what CF is for :) You are spot on using a content-based method to limit the universe of recommendations, and then ordering them via CF. Here's someone else talking about the same approach: https://www.quora.com/Recommendation-Systems-What-is-the-difference-between-item-to-item-collaborative-filtering-and-content-based-filtering/answer/Boaz-Ronkin?srid=tzM https://www.quora.com/Recommendation-Systems-What-is-the-dif... I love the idea of doing substitute identification (and complementary products). I made a pass at that some months ago by using some simple statistics to identify products that are never/rarely purchased together, in spite of their relative popularity. The idea being that no one is purchasing e.g. two tubes of different brands of toothpaste together in the same basket, even though both products are independently popular. The initial test led to mixed results, but I didn't pursue it far enough to know if the results would have ultimately been viable. I'd love to know what you find as you dig in.

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 ago
TF-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.

numlocked 10y ago

(article author here) Very interesting result. What techniques were you using for CF? You're right that buys:profiles ratio is a challenge, but dealing with a very sparse matrix like that is exactly what CF is for :) You are spot on using a content-based method to limit the universe of recommendations, and then ordering them via CF. Here's someone else talking about the same approach: https://www.quora.com/Recommendation-Systems-What-is-the-difference-between-item-to-item-collaborative-filtering-and-content-based-filtering/answer/Boaz-Ronkin?srid=tzM https://www.quora.com/Recommendation-Systems-What-is-the-dif... I love the idea of doing substitute identification (and complementary products). I made a pass at that some months ago by using some simple statistics to identify products that are never/rarely purchased together, in spite of their relative popularity. The idea being that no one is purchasing e.g. two tubes of different brands of toothpaste together in the same basket, even though both products are independently popular. The initial test led to mixed results, but I didn't pursue it far enough to know if the results would have ultimately been viable. I'd love to know what you find as you dig in.