GS$^{2}$-RS: A Generative Approach for Alleviating Cold start and Filter bubbles in Recommender Systems

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them bored and unsatisfied. The key to solving these issues is learning users' fine-grained preferences and recommending appealing and unexplored items deviating from users' historical items. However, existing models consider cold-start or filter bubble problems separately and ignore that they can reinforce mutually and damage the models' performance accuracy. To this end, we devise a novel serendipity-oriented recommender system ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> enerative <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elf-constrained <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> erendipitous <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ecommender <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ystem, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>-RS</b> ) that generates users' fine-grained preferences to enhance the recommendation performance. Specifically, GS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RS extracts users' interest and satisfaction preferences and generates virtual but convincible neighbors' preferences from themselves with a twin Conditional Generative Adversarial Nets (not from real neighbors). Then we introduce the serendipity item, which is low-interest but high-satisfaction among candidate items. We use the serendipity item to improve the diversity of recommended items, which relieves the filter-bubble problem. Along with this line, a gated mechanism is applied to their fine-grained preferences (interests, satisfactions) to obtain their serendipity items. Finally, these serendipity items are inversely injected into the original user-item rating matrix and build a relatively dense matrix as the input for backbone RS models. Note that GS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RS tackles cold-start and filter-bubble problems in a unified framework without any additional side information and enriches the interpretability of recommendation models. We comprehensively validate GS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RS for solving cold-start and filter bubble problems on four real-world benchmark datasets. Extensive experiments illustrate GS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> -RS's superiority in accuracy, serendipity, and interpretability over state-of-the-art models. Also, we can plug our model into existing recommender systems as a preprocessing procedure to enhance their performance.