The exponential growth of self-publishing and digital libraries has created a "paradox of choice" for readers. Traditional recommendation engines rely heavily on collaborative filtering and simple star ratings, which suffer from volume bias, rating inflation, and the "cold start" problem. This paper proposes eBookElo2 , a novel adaptation of the Elo rating system originally designed for chess, tailored specifically for the ranking of digital literature. Unlike static 5-star systems, eBookElo2 treats the selection of a book over available alternatives as a "tournament match." By integrating reading progression data and implicit feedback loops, eBookElo2 offers a more robust, anti-inflammatory, and dynamic method for literary ranking.
In chess, a match results in a win (1), loss (0), or draw (0.5). In eBookElo2, the "match" is the user's engagement. We define the Score ($S_A$) as a continuous variable based on implicit feedback: $$S_A = w_1 \cdot C + w_2 \cdot P + w_3 \cdot R$$ ebookelo2