An unexpectedness-augmented utility model for making serendipitous recommendation

Many recommendation systems traditionally focus on improving accuracy, while other aspects of recommendation quality are often overlooked, such as serendipity. Intuitively, a serendipitous recommendation is one that provides a pleasant surprise, which means that a suggestion must be unexpected to the user, and yet it must be useful. Based on this principle, we propose a novel serendipity-oriented recommendation mechanism. To model unexpectedness, we combine the concepts of item rareness and dis-similarity: the less popular is an item and the further is its distance from a user’s profile, the more unexpected it is assumed to be. To model usefulness, we adopt PureSVD latent factor model, whose effectiveness in capturing user interests has been demonstrated. The effectiveness of our mechanism has been experimentally evaluated based on popular benchmark datasets and the results are encouraging: our approach produced superior results in terms of serendipity, and also leads in terms of accuracy and diversity.