Personalized query auto-completion

Abstract

This invention provides an information processing device and method for automatically completing personalized queries. [Solution] The method for reranking search query candidates, executed by an information processing device, involves obtaining prefixes entered by a user on an e-commerce (EC) site, obtaining contexts that represent the user's search characteristics on the EC site, generating multiple search query candidates based on the prefixes, and reranking the multiple search query candidates using the results generated by inputting the multiple search query candidates and the contexts into a natural language processing model trained using information about the user's purchases on the EC site.

Claims

1. A learning data generation unit generates learning data from information on multiple purchased items and information on multiple unpurchased items among multiple items sold on an e-commerce (e-commerce) site. A learning unit that uses the aforementioned training data to train a natural language processing model on the user's purchasing tendencies on the e-commerce site, A reranking unit that uses the trained natural language processing model to rerank multiple search query candidates generated based on the string input by the user, An information processing device having

2. The system further includes a context acquisition unit that acquires a context representing the characteristics of the user's search on the aforementioned e-commerce site. The reranking unit reranks the multiple search query candidates using the results generated by inputting the multiple search query candidates and the context into the learned natural language processing model. The information processing apparatus according to claim 1.

3. The context acquisition unit acquires the search query that was last entered by the user before the input of the string on the e-commerce site as the context. The information processing apparatus according to claim 2.

4. The training data generation unit generates training data that includes positive pairs, which are pairs of information of two items from the plurality of purchased items, and negative pairs, which are pairs of information of two items from the plurality of items, where at least one of them is information of one of the plurality of non-purchased items. The aforementioned learning unit, Each of the positive pairs is input to the natural language processing model to generate a feature vector for the positive pair, and each of the negative pairs is input to the natural language processing model to generate a feature vector for the negative pair. The natural language processing model is trained to minimize the distance between the positive pair of feature vectors and maximize the distance between the negative pair of feature vectors in a common vector space. The information processing apparatus according to claim 1.

5. The information for the two items mentioned above is the brand name of the two items. The information processing apparatus according to claim 4.

6. The aforementioned reranking unit is Each of the multiple search query candidates is input into the trained natural language processing model to generate multiple query feature vectors representing the characteristics of each of the search query candidates, and the context is input into the trained natural language processing model to generate a context feature vector representing the characteristics of the context. In a common vector space, the multiple search query candidates are reranked based on the distance between each of the multiple query feature vectors and the context feature vector. The information processing apparatus according to claim 2.

7. The reranking unit reranks the plurality of search query candidates so that they are arranged in order from the query feature vectors with the shortest distance from the context feature vector among the plurality of query feature vectors. The information processing apparatus according to claim 6.

8. The aforementioned natural language processing model is a pre-trained BERT (Bidirectional Encoder Representations from Transformers). The information processing apparatus according to claim 1.

9. The system further includes an output unit that provides the reranked search query candidates to the user. The information processing apparatus according to claim 1.

10. An information processing method performed by an information processing device, This involves generating training data from information on multiple items purchased by a user and information on multiple items not purchased, among the multiple items sold on an e-commerce (e-commerce) site. Using the aforementioned training data, a natural language processing model is trained to understand the user's purchasing tendencies on the e-commerce site. Using the trained natural language processing model, the system re-ranks multiple search query candidates generated based on the string input by the user. Information processing methods including

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