Semantic News Recommendation Using WordNet and Bing Similarities

While traditionally content-based news recommendation was performed using the word vector space model, more recent approaches also take into account semantics, often through the use of semantic lexicons. However, named entities are rarely taken into account, as they are often absent in such lexicons. Nevertheless, they can play a crucial role in determining user interest for specific news articles. Therefore, in this work, we extend the state-of-the-art semantic lexicon-driven Semantic Similarity (SS) recommendation method by additionally considering named entities. First, as in SS, we calculate similarities between WordNet synonym sets in unread news items and synonym sets in read news items (stored in user profiles). Then, we use the page counts of named entities that are retrieved from the Bing Web search engine to compute named entity similarities between unread and read news items. Results show that our recommendation method, BingSS, outperforms SS in terms of F1, precision, accuracy, and specificity.