Social recommender systems are algorithms that leverage social network data and user interactions to suggest items, services, or content to users. These systems analyze relationships and preferences within a social context to enhance personalization and improve the relevance of recommendations.
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Social recommender systems are algorithms that leverage social network data and user interactions to suggest items, services, or content to users. These systems analyze relationships and preferences within a social context to enhance personalization and improve the relevance of recommendations.
We test the impact of integrating a measure of {\em common friendship} in collaborative filtering, in order to capture the intuition that socially interconnected groups of people tend to have similar tastes. An experiment on the Yelp... more
We test the impact of integrating a measure of {\em common friendship} in collaborative filtering, in order to capture the intuition that socially interconnected groups of people tend to have similar tastes. An experiment on the Yelp dataset shows that using preference information derived from the commonalities of interests in networks of friends achieves higher accuracy than item-to-item collaborative filtering.