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As natural language interfaces enable users to express increasingly complexnatural language queries, there is a parallel explosion of user review contentthat can allow users to better find items such as restaurants, books, or moviesthat match these expressive queries. While Neural Information Retrieval (IR)methods have provided state-of-the-art results for matching queries todocuments, they have not been extended to the task of Reviewed-Item Retrieval(RIR), where query-review scores must be aggregated (or fused) into item-levelscores for ranking. In the absence of labeled RIR datasets, we extend Neural IRmethodology to RIR by leveraging self-supervised methods for contrastivelearning of BERT embeddings for both queries and reviews. Specifically,contrastive learning requires a choice of positive and negative samples, wherethe unique two-level structure of our item-review data combined with meta-dataaffords us a rich structure for the selection of these samples. For contrastivelearning in a Late Fusion scenario, we investigate the use of positive reviewsamples from the same item and/or with the same rating, selection of hardpositive samples by choosing the least similar reviews from the same anchoritem, and selection of hard negative samples by choosing the most similarreviews from different items. We also explore anchor sub-sampling andaugmenting with meta-data. For a more end-to-end Early Fusion approach, weintroduce contrastive item embedding learning to fuse reviews into single itemembeddings. Experimental results show that Late Fusion contrastive learning forNeural RIR outperforms all other contrastive IR configurations, Neural IR, andsparse retrieval baselines, thus demonstrating the power of exploiting thetwo-level structure in Neural RIR approaches as well as the importance ofpreserving the nuance of individual review content via Late Fusion methods.

Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval