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Time is an important relevance signal when searching streams of social mediaposts. The distribution of document timestamps from the results of an initialquery can be leveraged to infer the distribution of relevant documents, whichcan then be used to rerank the initial results. Previous experiments have shownthat kernel density estimation is a simple yet effective implementation of thisidea. This paper explores an alternative approach to mining temporal signalswith recurrent neural networks. Our intuition is that neural networks provide amore expressive framework to capture the temporal coherence of neighboringdocuments in time. To our knowledge, we are the first to integrate lexical andtemporal signals in an end-to-end neural network architecture, in whichexisting neural ranking models are used to generate query-document similarityvectors that feed into a bidirectional LSTM layer for temporal modeling. Ourresults are mixed: existing neural models for document ranking alone yieldlimited improvements over simple baselines, but the integration of lexical andtemporal signals yield significant improvements over competitive temporalbaselines.

Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams