一种融合个性化与多样性的人物标签推荐方法

针对人物标签推荐中多样性及推荐标签质量问题,该文提出了一种融合个性化与多样性的人物标签推荐方法。该方法使用主题模型对用户关注对象建模,通过聚类分析把具有相似言论的对象划分到同一类簇;然后对每个类簇的标签进行冗余处理,并选取代表性标签;最后对不同类簇中的标签融合排序,以获取Top-K个标签推荐给用户。实验结果表明,与已有推荐方法相比,该方法在反映用户兴趣爱好的同时,能显著提高标签推荐质量和推荐结果的多样性。

User Tag Recommendation with Personalization and Diversity

To take full advantage of users social characteristics and address the diversity of tag recommendation, we present a method for user tag recommendation, aiming to combine users social characteristics and the diversity of tag recommendation. We use topic model to get a users potential semantic topics from his tweets, and then cluster the users followed by this user, i.e. using the potential semantic topics to divide the users into different areas. Each area can reflect the interest that attracts the user to follow. We select several representative tags by sorting the tags in the area based on TF-IDF. Then, we combine and sort different areas of representative tags to get top-K tags for recommendation. Experiment shows that our approach not only can recommend diversity tags but also reflect the users interest and hobbies.