Addressing the challenge of matching queries with the right experts amid temporal-textual inconsistencies, we present a novel approach that combines an attention-based text embedding model with a continuous-time module. This method effectively maps queries to relevant experts by analyzing concept-oriented vectors and user behavior, demonstrating significant effectiveness on StackOverflow and Yahoo datasets.

Finding the most suitable individual to answer a question using brief content has important usages, including the community of question answering systems and online recommender frameworks. However, one must tackle challenges: Disregarding the indispensable noise in short text contents, authors usually answer the input query with mismatched words that can negatively influence the textual relevance. Moreover, many vocabularies imply various alterations. Finally, not every expert is eager to answer an input query given the time constraint, named the reluctance dilemma. To overcome the challenges, we devise a novel embedding approach that constructs context-aware vectors. We then extract the knowledge domains out of the online contextual content. While we track user textual-temporal behavioral patterns via an infinite continuous-time module, we recommend a set of experts pertinent to the given query and willingly provide the response during the expected time. Experimental results on two real-world datasets ofStackOverflowandYahooshow that our online time-sensitive value-wise transformer can achieve higher effectiveness and efficiency versus other trending rivals in online expert recommendation systems. In addition, we empirically experience that Fourier transformers can automatically infer multi-aspect base signals and overpass manual discrete-time models in obtaining time-specific user profiles.