Abstract: Systems and methods are used to detect underlying themes from a collection of documents at an aggregated level. A representative set of documents may be selected from a cluster of documents, with the representative set of documents corresponding to a general theme of the cluster. Candidate theme phrases may then be extracted from the documents and used to generate document embeddings and candidate phrase embeddings, which may be ranked, such as with a diversity-based ranking approach. Certain candidates may be selected from the ranking. Each of the documents forming the representative set may then be concatenated and a query embedding may be generated and ranked against the candidate phrases. In this manner, a collection of phrases associated with both the general underlying theme of the cluster, along with granular topics associated with that theme, may be identified.

Abstract: The present disclosure generally relates to a feedback processing service that can receive customer input, as customer feedback corresponds to a service context. The feedback processing service aggregates semantically similar feedback as a cluster. Then, the feedback processing service can prioritize each of the clusters by ranking each of the clusters.

Abstract: Generally, the present disclosure relates to methods and systems for generating complementary product recommendations. In some example aspects, human-identified complementarity of a subset of products can be used to train a neural network, which is in turn used to generate pairwise complementarity values for items. Based on such values, complementary items can be identified.

Abstract: Generally, the present disclosure relates to methods and systems for generating complementary product recommendations. In some example aspects, human-identified complementarity of a subset of products can be used to train a neural network, which is in turn used to generate pairwise complementarity values for items. Based on such values, complementary items can be identified.

Abstract: Methods and systems for managing an item assortment from among a collection of heterogeneous items are disclosed. One method includes receiving item data associated with the collection of heterogeneous items that defines values for a plurality of item attributes, and calculating a score for a degree of substitutability between items. A community detection algorithm is applied to edge weights that are based on the scores between items, to identify substitution groups among the items. Preferred attributes common to items within the substitution groups are found, and an item assortment is updated based on a determination of substitutability among items in at least one of the substitution groups.