Tag Recommendation Method for Enhancing Web Novel Retrieval

Abstract

A huge number of web novels i.e. user-generated novels, exist on the Internet. The increase in their number has cased crucial problems for both writers and readers; even if noteworthy, newly submitted novels are buried amidst existing ones and readers have difficulty locating suitable, relevant novels. Most web novel sites attach several tags to each novel to describe its themes, characters, genre and so on. These tags generally vary widely because they are attached by the individual writer without specific rules or uniformity. In this paper, we propose tag recommendation methods that enable readers to easily retrieve suitable novels. These recommended tags should express the contents of the novel, differ from each other, and be selected from tags attached to a sufficiently limited number of novels. To satisfy these requirements, we analyze the tag distributions in an actual web novel site. We vectorize the novels using Dov2Vec methods, and vectorize the tags from the novel’s vectors attached to the novels.