Nov 26
Search engines are hard workers. They are our information retrieval slaves. Are we satisfied with them? Do they a good job for us? ‘Yes’, would be the most quoted answer, I guess. But what does ‘most’ mean here? Does it mean 99%, 80% or 50.0003%? Giving an answer to this question implies to measure user satisfaction and we all know: that’s tough. Because of this, one is not in a position to give definitive answers.
However, there are some studies giving evidence, that 30%-60% are rather frustrated and therefore NOT satisfied. Before giving some reasons, I quote some interesting figures: Continue reading »
Tagged with: SearchEngine
Nov 17
Yes we all know: ratings from users are very noisy and not consistent. If you ask users to re-rate items they will do it differently in most cases. This was pointed out by a number of scientists. Knowing and removing noise could lead to better prediction performance. One simple approach to reduce noise is achieved by asking people to re-rate all objects they have rated so far. This was also pointed out by technocalifornia.
However, I doubt that users are very happy to re-rate everything. So, how can we learn and remove noise? Currently I try to model user ratings, taking into account inconsistency and peer influence. I hope the model will give some insights, when compared to real world data generated by recommender systems.
Tagged with: RecommenderSystems
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