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--- intelligence_engine:product_recommendation_by_lightfm [2019/06/26 04:49]
182.72.26.6
+++ intelligence_engine:product_recommendation_by_lightfm [2019/07/02 10:04] (current)
182.72.26.6
@@ Line 8: / Line 8: @@
   * It provides recommendations for both implicit and explicit data where as many other libraries support only explicit data.
   * It provides a facility to track metrics like model accuracy,precision which is very helpful to understand the recommendation accuracy.
-  * It can make recommendation by considering the user and item features data.
+  * It can make recommendation by considering the user features(job_role,city,org_id) data.
 ===== Implementation =====
@@ Line 15: / Line 15: @@
 Lightfm is a popular recommendation algorithms for both implicit and explicit feedback data.It incorporates both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).Lightfm implementation is very easy and has enough documentation in the below link
-[[http://lyst.github.io/lightfm/docs/home.html.]]
+[[http://lyst.github.io/lightfm/docs/home.html]]
 Lightfm implementation can be divided into following steps

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