User 5174 | 5/5/2016, 12:05:46 PM
I have been using GLAB item similarity recommender with "pre-computed similarity matrix between items" in order to build a pure content-based RS recently: https://dato.com/products/create/docs/generated/graphlab.recommender.itemsimilarityrecommender.ItemSimilarityRecommender.html
In one of the experiments I realized when my item features are all zero (the corresponding similarity matrix is all 1), GLAB produces very good results! This came surprising to me how with all similar similarities, GLAB can produce such good results, it came strange and very surprising to me!
After checking the result of recommendation, I noticed the recommendations for all users are almost identical. The items recommended contained also high number of ratings. This made me believe that "recommendation based on popularity of items" is automatically chosen by the algorithm in the scenarios I described above. Though at first look, this may be good, but when evluating the performance of my RS I need to make sure that any results obtained are the result of content future solely and not other factors. Now, I am not sure when GLab uses recommendation based on popular items (where popular items being the ones with highest number of ratings)
Is there anyway in Glab I can switch off "recommendation based on popular items" when I am using itemsimilarityrecommender ? I prefer to have NO recommendation or random recommendation when I am using it with item similarities ALL equal.
It would be great if you could clarify this point.