Modeling User Repeat Consumption Behavior for Online Novel Recommendation

TL;DR

This paper introduces NovelNet, a neural network model for online novel recommendation that leverages fine-grained interaction features and reconsumption patterns, especially for new users, and provides a new benchmark dataset.

Contribution

The paper proposes NovelNet, a novel neural network architecture that improves recommendation accuracy for new users by modeling detailed interactions and reconsumption behavior.

Findings

NovelNet outperforms baseline models in accuracy.

Fine-grained interaction features enhance prediction quality.

The new dataset supports further research in online novel recommendation.

Abstract

Given a user's historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is important but underexplored. In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days. We have two observations about online novel recommendation for new users. First, repeat novel consumption of new users is a common phenomenon. Second, interactions between users and novels are informative. To accurately predict whether a user will reconsume a novel, it is crucial to characterize each interaction at a fine-grained level. Based on these two observations, we propose a neural network for online novel recommendation, called NovelNet. NovelNet can recommend the next novel from both the user's consumed novels and new novels simultaneously. Specifically, an interaction encoder is used to obtain accurate interaction representation considering fine-grained attributes of interaction, and a pointer network with a pointwise loss is incorporated into NovelNet to recommend previously-consumed novels. Moreover, an online novel recommendation dataset is built from a well-known online novel reading platform and is released for public use as a benchmark. Experimental results on the dataset demonstrate the effectiveness of NovelNet.