Abstract: A system that recommends literary works to a user based on identified trends of how text in the literary works liked and/or disliked by the user are written and/or structured is provided. For example, the system may analyze the text of a literary work to identify one or more metrics. Based on the identified metrics, the system can generate an analytical summary called a fingerprint for the literary work. The ratings assigned to literary works by the user may be used in conjunction with the generated fingerprints to generate positive and/or negative models for the user. The positive model captures aspects of literary works that the user likes and the negative model captures aspects of literary works that the user dislikes. The system can then compare some or all of the generated fingerprints in a literary works fingerprint database with the positive and/or negative models to select literary works to recommend to the user.

Abstract: A system that recommends literary works to a user based on identified trends of how text in the literary works liked and/or disliked by the user are written and/or structured is provided. For example, the system may analyze the text of a literary work to identify one or more metrics. Based on the identified metrics, the system can generate an analytical summary called a fingerprint for the literary work. The ratings assigned to literary works by the user may be used in conjunction with the generated fingerprints to generate positive and/or negative models for the user. The positive model captures aspects of literary works that the user likes and the negative model captures aspects of literary works that the user dislikes. The system can then compare some or all of the generated fingerprints in a literary works fingerprint database with the positive and/or negative models to select literary works to recommend to the user.