Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating interaction and network asset association predictions. Recommendations for network assets in response to interactions with other network assets may be provided. Initially, a pair of items, including a seed asset and a candidate asset, is received. Each word in the seed and candidate titles, each aspect, and the categories may be embedded into a k-dimensional vector space. The embedding may then be aggregated to construct an n-dimensional vector representing a seed asset and an n-dimensional vector representing a candidate asset which are used to determine and generate a probability that the seed asset and the candidate asset are contemporaneously operated upon by the same user. The system may then rank recommendation candidates by a co-interaction probability output of the neural network system.

Abstract: A system and method to evaluate the affinity of a collection of sale items to a user's interests. The affinity is a measure of how closely a user's interests match the contents of a collection (e.g., a collection of items selected by a seller, other user, or employee of the sales site). The method may determine the affinity of various collections by using a vector-space distance measure between the user's categories of interest and the relative percentages of various categories of items in each collection's. The method may also add a quality score for the collection to the affinity score and/or a random value to ensure that the system recommends high quality collections does not recommend the same set of collections every time the user logs in or visits the sales site.

Abstract: Systems, methods and media are provided for optimizing similar item recommendations in a semi-structured environment. In one embodiment a system includes at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising, at least identifying a seed item; retrieving a subset of recommended items relevant to the seed item; and ranking the subset of recommended items based on an item conversion probability, wherein the ranking of the subset of recommended items is based on a machine learning technique, and wherein a binary or multi-class label is used as training input to the machine learning technique.