Abstract: Traditional lexicon-based news recommendation algorithms often ignore the emotional nuances present in words beyond the confines of the dictionary. This oversight can lead to issues such as diminished prediction accuracy and subpar sorting performance. To address these challenges, this paper introduces a heuristic approach to deduce the sentiment of unfamiliar words and devises a news recommendation algorithm to verify its feasibility. A tripartite graph model is constructed to propagate sentiment from a sentiment dictionary to individual words and obtain the headline sentiment. In addition, the bag-of-words model is used to extract topic features from the headlines. The sentiment similarity and topic similarity between headlines are calculated, consolidating these into a comprehensive similarity evaluation index. The news with higher similarity to the target news is then selected as the neighbor. The algorithm predicts the hourly average click volume of the target news by considering the hourly average click volume of neighbors, treating this as the predicted score for the target news. Finally, users receive a selection of high-scoring news articles. Validation using real data from NetEase News confirms the feasibility and effectiveness of our algorithm. Compared with other algorithms, our algorithm has shown improvements in the optimal accuracy of mean absolute error (MAE) by 2.2% to 3.4%, root mean square error (RMSE) by 2.3% to 2.9%, and the mean score of normalized discounted cumulative gain (NDCG) by 0.7% to 1.8%, respectively.

Key words: recommender system, sentiment analysis, bag-of-words, collaborative filtering, tripartite graph