Our Research

sequence or pseudo-sequence?

Sequential recommendation aims to model a user’s preferences by looking at the order of interactions in a user’s history. The evaluation of such algorithms requires robust datasets with genuine sequential information. In this work we analyze the timestamp information of several commonly used datasets and show that reported timestamps are not indicative of meaningful sequential order. In the datasets explored, significant numbers of users have interactions occurring at identical timestamps. The actual order of these interactions is therefore unknowable; the interaction history is pseudo-sequential. We find that randomly shuffling the order of interactions has minimal impact on the performance of a leading sequential recommender. Particular attention is paid to MovieLens because of its frequency of use in the field of sequential recommendation. Our findings motivate the necessity for new datasets with more meaningful ordering for the evaluation of sequential recommenders.


Narrative analysis is becoming increasingly important for a number of linguistic tasks in- cluding summarization, knowledge extraction, and question answering. We present a novel approach for narrative event representation us- ing attention to re-contextualize events across the whole story. Comparing to previous analy- sis we find an unexpected attachment of event semantics to predicate tokens within a popu- lar transformer model. We test the utility of our approach on narrative completion predic- tion, achieving state of the art performance on Multiple Choice Narrative Cloze and scoring competitively on the Story Cloze Task.

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