5 results about "Recommendation quality" patented technology

The invention discloses a personalized recommendation method based on differential privacy and federated learning, which comprises the following steps of: firstly, collecting user behavior data and carrying out preprocessing and behavior modeling on the user behavior data, and then adopting a dynamic privacy budget allocation and differential privacy noise injection mechanism in a local training stage of federated learning; meanwhile, an adaptive gating layer is introduced to cut low-importance parameters, then data modeling and personalized model training are carried out based on a Gaussian mixture model, finally, global aggregation of federated learning is carried out by adopting a personalized model aggregation strategy, and a global model is dynamically updated according to data distribution of different clients. And the local personalized recommendation effect is ensured. According to the method, the problems of privacy protection, overlarge calculation and communication overhead, data heterogeneity and the like in a personalized recommendation system are effectively solved, the model training efficiency and the recommendation quality are improved, and the method is suitable for multiple fields of e-commerce, social platforms, video recommendation and the like and has wide application value.

The invention provides an AI platform data recommendation method and device and computer equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining a historical consultation dialogue record set of a target user, carrying out progressive multi-round dialogue iterative memory analysis, and determining the understanding category of the target user; obtaining a real-time active reasoning dialogue sequence; when a backtracking detection module is called to carry out recommended data completeness detection on the real-time active reasoning dialogue sequence, a recommended data completeness detection result is obtained; and triggering a recommendation output instruction according to the recommendation data completeness coefficient, and pushing recommendation data based on the recommendation output instruction. The data recommendation method and device have the advantages that the technical problems that in the data recommendation process in the prior art, in-depth interaction with the user cannot be dynamically conducted, initiative questioning in accordance with the actual situation of the user cannot be achieved, the recommendation quality is low, and the real-time requirement of the user cannot be met easily are solved, and the technical effect of improving the data recommendation quality is achieved.

The invention discloses a commodity recommendation method based on big data, and the method comprises the steps: collecting data, recognizing the credibility of the data, carrying out the statement analysis of the data based on a big model, and generating an information credibility score; calculating attribute similarity and semantic similarity, calculating basic similarity, and correcting the basic similarity to obtain final similarity; a weight mapping table is set, the system performs automatic query according to commodity categories, obtains an optimal weight combination and performs descending sort according to final similarity to obtain a similar commodity recommendation table, and a large model is used for generating an explanatory reason for a recommendation result in the similar commodity recommendation table; when similar commodities are insufficient or are new commodities, a popularity substitution strategy is used, mixed calculation of attribute similarity and semantic similarity is adopted, recommendation accuracy is improved, information credibility scores are introduced to serve as weight factors, similarity calculation is more accurate, interference of low-credibility commodities on recommendation results is reduced, and recommendation quality is improved.

The application discloses a method, device, storage medium and electronic equipment for generating a recommendation list. The method comprises the following steps: constructing a project structure diagram containing a first object and a target project, determining a connection matrix between the first object and the target project from the project structure diagram; calculating the similarity between the first object and other objects according to the connection matrix; obtaining a preset similarity, screening other objects based on the preset similarity and the similarity, and obtaining a second object set similar to the first object; connecting the first object and each second object in the second object set in a graph to obtain a relationship graph of the first object and each second object; obtaining a relationship matrix of the object and the target project, and generating a recommendation list according to the relationship graph and the relationship matrix. The application solves the technical problems of inaccurate recommendation results, poor recommendation quality and cold start caused by ignoring the edge information of users or projects in the process of performing a recommendation task in the related art.

The application relates to a heterogeneous collaborative filtering method for multi-behavior recommendation, belongs to the multi-behavior recommendation field, and is based on a heterogeneous collaborative filtering model HCFMR for multi-behavior recommendation and comprises the following steps: S1, obtaining user and item interaction features of a specific behavior by using an improved lightweight graph convolutional network; S2, obtaining multi-behavior dependent semantics of a user bottom layer by using a multi-head attention network; S3, setting a weight of a specific behavior for each user, and the weight is used for distinguishing the contribution degrees of different behaviors; and S4, obtaining high-order embedding representation of a node by aggregating various convolutional layers, obtaining a user preference, and then performing multi-behavior recommendation. The application reduces model complexity, enriches user and item embedding representation, improves recommendation quality, and improves the explainability of the model.