Advertising is becoming a business on social networks. Billions of people around the world use social media, and fastly, it has become one of the defining technologies of our time. Social platforms like Twitter are one of the primary means of communication and information dissemination and can capture the interest of potential customers. Therefore, it is crucial to select suitable advertisements to users in specific times and locations for capturing their attention, profitably. In this paper, we propose a context-aware advertising recommendation system that, by analyzing the users' tweets and movements along a timeline, infers the personal interests of users and provides attractive ads to users through the triadic formal concept analysis theory.

No abstract

No abstract

Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the stateof-the-art algorithms in terms of F 1 − measure, M AP , and N DCG.