What are some emerging trends and techniques for serendipitous recommendations?

1 What is serendipity and why is it important?

Serendipity is a complex and multidimensional concept that can be defined in different ways. According to a widely used definition by Mark and André (2011), serendipity is "the experience of finding information that is both unexpected and valuable". Serendipity can occur in various domains, such as science, art, literature, and everyday life. In recommender systems, serendipity can be seen as a desirable property that can improve user experience and satisfaction. Serendipitous recommendations can help users discover new items, genres, topics, or perspectives that they might not have considered or encountered otherwise. Serendipitous recommendations can also increase user trust, diversity, and exploration in recommender systems.

2 How can recommender systems generate serendipitous recommendations?

When generating serendipitous recommendations, there is no single or definitive approach; it depends on the type, context, and goal of the recommender system, as well as the user preferences, behavior, and feedback. However, some common techniques include diversifying the recommendation list by adding items that are dissimilar or orthogonal to the user's profile or history but still relevant and interesting. Personalizing the list by taking into account the user's personality, mood, context, or serendipity preference is also useful. Additionally, exploiting the social or collaborative aspects of recommender systems by leveraging information from other users, groups, or communities can be beneficial. Finally, incorporating serendipity triggers or cues into the recommendation interface can help highlight features that make an item serendipitous.

3 How can recommender systems measure serendipitous recommendations?

Measuring serendipity in recommender systems is a complex and unsolved problem. There are various metrics and methods that capture different aspects of serendipity, like unexpectedness, relevance, satisfaction, novelty, diversity, or discovery. To measure serendipitous recommendations, one can use user feedback or ratings to assess the user's reaction to the recommended items. Additionally, user behavior or actions can be used to infer the user's response or engagement with the recommended items. Finally, algorithmic or analytical measures may be used to quantify the degree of serendipity in the recommendation list.

4 What are some examples of serendipitous recommender systems?

Serendipitous recommender systems can be found in various domains and applications, such as e-commerce, entertainment, education, or research. For instance, Amazon's "Customers who bought this item also bought" feature can expose users to new products or categories that they might not have searched for. Netflix's "Because you watched" feature can introduce users to new genres, actors, directors, or themes. Spotify's "Discover Weekly" feature creates a personalized playlist of songs tailored to the user's music taste but can also surprise them with new artists, tracks, or genres. Coursera's "Recommended for you" feature suggests courses relevant to the user's interests and goals, but can also inspire them to learn new topics or disciplines.