On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms

Recommender systems are based on inherent forms of social influence. Indeed, suggestions are provided to the users based on the opinions of peers. Given the relevance that ratings have nowadays to push the sales of an item, sellers might decide to bribe users so that they rate or change the ratings given to items, thus increasing the sellers’ reputation. Hence, by exploiting the fact that influential users can lead an item to get recommended, bribing can become an effective way to negatively exploit social influence and introduce a bias in the recommendations. Given that bribing is forbidden but still employed by sellers, we propose a novel matrix completion algorithm that performs hybrid memory-based collaborative filtering using an approximation of Kolmogorov complexity. We also propose a framework to study the bribery effect and the bribery resistance of our approach. Our theoretical analysis, validated through experiments on real-world datasets, shows that our approach is an effective way to counter bribing while, with state-of-the-art algorithms, sellers can bribe a large part of the users.