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One of the key challenges in Sequential Recommendation (SR) is how to extractand represent user preferences. Traditional SR methods rely on the next item asthe supervision signal to guide preference extraction and representation. Wepropose a novel learning strategy, named preference editing. The idea is toforce the SR model to discriminate the common and unique preferences indifferent sequences of interactions between users and the recommender system.By doing so, the SR model is able to learn how to identify common and uniqueuser preferences, and thereby do better user preference extraction andrepresentation. We propose a transformer based SR model, named MrTransformer(Multi-preference Transformer), that concatenates some special tokens in frontof the sequence to represent multiple user preferences and makes sure theycapture different aspects through a preference coverage mechanism. Then, wedevise a preference editing-based self-supervised learning mechanism fortraining MrTransformer which contains two main operations: preferenceseparation and preference recombination. The former separates the common andunique user preferences for a given pair of sequences. The latter swaps thecommon preferences to obtain recombined user preferences for each sequence.Based on the preference separation and preference recombination operations, wedefine two types of SSL loss that require that the recombined preferences aresimilar to the original ones, and the common preferences are close to eachother.  We carry out extensive experiments on two benchmark datasets. MrTransformerwith preference editing significantly outperforms state-of-the-art SR methodsin terms of Recall, MRR and NDCG. We find that long sequences whose userpreferences are harder to extract and represent benefit most from preferenceediting.

Improving Transformer-based Sequential Recommenders through Preference Editing