Recommender Systems Questions
Novelty-aware recommender systems are designed to address the issue of user boredom or fatigue caused by repeatedly recommending the same popular or commonly known items. These systems aim to provide recommendations that not only satisfy user preferences but also introduce novel or diverse items that the user may not have encountered before.
The concept of novelty in recommender systems refers to the degree of newness or unfamiliarity of recommended items to the user. Novelty-aware recommender systems consider the user's past interactions, preferences, and historical data to identify items that are both relevant to the user's interests and have a certain level of novelty.
To achieve this, these systems employ various techniques such as content-based filtering, collaborative filtering, or hybrid approaches. They may use algorithms that balance between exploiting the user's known preferences and exploring new items to recommend. This can be done by incorporating diversity measures, serendipity metrics, or novelty scores into the recommendation process.
By incorporating novelty into the recommendation process, these systems aim to enhance user satisfaction and engagement by introducing them to new and interesting items that they may not have discovered on their own. This can lead to a more personalized and diverse user experience, promoting user exploration and discovery of different items within their preferred domain.