<p>You can use <a href="https://arxiv.org/abs/1205.2618" rel="nofollow noreferrer">Bayesian Personalized Ranking for implicit feedback</a>. I wrote about my experience of building such <a href="https://medium.com/heyjobs-tech/building-recommendation-system-based-bayesian-personalized-ranking-using-tensorflow-2-1-b814d2704130" rel="nofollow noreferrer">recommendation systems using Tensorflow</a>.</p>
<p>Regarding timeliness, you should use only active items to find recommendations.</p>
<p>For an example of the workflow can look like:</p>
<ul>
<li>you need to recommend 5 items;</li>
<li>you ask the system to give you 30 recommendations using only active items;</li>
<li>then exclude items that will expire in the next 2 days;</li>
<li>then randomly select 5 from those who stayed;</li>
</ul>
How to build a Recommendation System using Implicit dataset with strong timeliness?
<p>I have a dataset that has, <strong>users</strong>, <strong>items</strong> and <strong>views</strong>,which is the interaction between user and item.</p>
<p>The only difference in this dataset from the other recommendation datasets is that, the items have strong timeliness i.e. the items expires after a certain time period and won't be considered anymore.(Items life span can range from 1 week - 4 months)</p>