Hi all. In a recommender, it is quite common to give a higher priority to newer items. For example, when User A has created a tweet yesterday and another tweet today, it is better to recommend the newer one to the other users (assume the 2 tweets has similar property except creation date)
Specifically for Universal Recommender, can we set such a bias to new items? I have read through the doc and some source code, but the best option I can find is to set the hard limit "dataRange". Is it possible to set a smooth decay to the bias? Or can I modify the source code to achieve this functionality? I notice that elasticsearch has an option function_score query: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html Which seems to offer a smooth decay of bias, but I have no idea if it can be integrated to Universal Recommender and solve the problem. Can anyone point out if it looks feasible? And I also notice that in some old version of PredictionIO, there was a parameter called freshness. See this commit: https://github.com/apache/incubator-predictionio/commit/4bebe7855767532fd4d2660548d32ab1581270d8 And this blog on freshness: http://blog.monokkel.io/introduction-to-predictionio-by-example/ Is "freshness" now removed? Sorry for asking so many question at a time. The community here is great. Thanks in advance! Best, Brain
