Suppose you work for a business that has paying customers. You want to know how much money your customers are likely to spend to inform decisions on customer acquisition and retention budgets. You’ve done a bit of research, and discovered that the figure you want to calculate is commonly called the customer lifetime value. You google the term, and end up on a page with ten results (and probably some ads)....
This is the second and last post summarising my team’s solution for the Yandex search personalisation Kaggle competition. See the first post for a summary of the dataset, evaluation approach, and some thoughts about search engine optimisation and privacy. This post discusses the algorithms and features we used. To quickly recap the first post, Yandex released a 16GB dataset of query & click logs. The goal of the competition was to use this data to rerank query results such that the more relevant results appear before less relevant results....
About a year ago, I participated in the Yandex search personalisation Kaggle competition. I started off as a solo competitor, and then added a few Kaggle newbies to the team as part of a program I was running for the Sydney Data Science Meetup. My team hasn’t done too badly, finishing 9th out of 194 teams. As is usually the case with Kaggle competitions, the most valuable part was the lessons learned from the experience....
In previous posts about getting traction for my Bandcamp recommendations project (BCRecommender), I mentioned search engine optimisation (SEO) as one of the promising traction channels. Unfortunately, early efforts yielded negligible traffic – most new visitors came from referrals from blogs and Twitter. It turns out that the problem was not showing up for the SEO game: most of BCRecommender’s pages were blocked for crawling via robots.txt because I was worried that search engines (=Google) would penalise the website for thin/duplicate content....