The SearchStar 2019 conference on Analytics of Conversion was held in Bath last 27th June, organised by Search Star , an UK-based company founded in 2005 and focused on advancing digital strategies for marketing and advertisement.
Ivan Palomares from DSRS was one of the speakers at the event, where he delivered the talk Personalisation & Recommender Systems: Perspectives and Challenges.
A summary of the conference, including Ivan’s talk, was published in Search Star blog:
Iván offered his audience an insight into the revolutionary world of recommender systems and the role it will play in consumer choice in the future. Iván started by presenting the audience with a problem – ‘you want to buy a new book.’ Twenty years ago, we would have gone to a neighbourhood bookshop to ask for a recommendation. Nowadays, with the same question, we turn to the internet and are instantly overwhelmed by an extensive amount of results.
What’s the solution? Personalistion through recommender systems. These are evolutionary algorithms designed to provide tailored content to the user, by extracting knowledge about your preferences from previous choices and choices of similar users. If you’ve ever listened to songs on Spotify or bought from Amazon, then these recorded decisions are used to inform these systems about your preferences.
There are many techniques used to tailor these recommendations; you could either be served results based on your demographic, where you are at a certain time and the opening hours of the shops around you (context-aware), or choosing a selection of restaurants based on the reasoning that people with similar interests to you also enjoyed those restaurants (collaborative filtering).
There are challenges to this approach, specifically when they come across a ‘cold user,’ where they lack information on their preferences, or the essential requirement to combine multiple views and sources of user data.
We also had a significant question from the audience regarding data privacy, however Iván responded that all data used to make the recommendations are information that the user has provided either explicitly or implicitly, and it is on the usage of implicit user data where privacy and ethics concerns really come into scene.
Our team member was also interviewed prior to his talk. You can read the interview, entitled ‘What are Recommender Systems?’, here
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