9 results about "Preference weight" patented technology

The embodiment of the invention provides a decision negotiation method and device based on a depth deterministic policy gradient. The method comprises the following steps: receiving negotiation scene information input by a first negotiation party; wherein the negotiation scene information comprises a plurality of negotiation topics, a preference value interval for each negotiation topics and a preference weight value for each negotiation topics; for each negotiation topic, creating a invention for the negotiation topic according to the preference value interval and the preference weight value of the negotiation topic; wherein the utility model comprises corresponding trapezoidal fuzzy membership functions when negotiation values are in different preference value intervals, and an overall satisfaction function for performing weighted summation on the trapezoidal fuzzy membership functions of all negotiation dimensions; a first reference value of the first negotiation party is predicted through a pre-trained Actor-Critic network; and receiving a first negotiation value input by the first negotiation party according to the first reference value, and sending the first negotiation value to a second negotiation party, thereby facilitating determination of the negotiation value.

The invention provides a commodity recommendation method and system based on consumer behaviors, and the method comprises the steps: firstly extracting explicit behavior features and implicit preference features of consumers in different context scenes, and determining node preference indexes of consumption behaviors in different context scenes; generating a short-term intention chain and a long-term preference chain of the consumer according to the node preference index, and further constructing an element feature map reflecting consumer consumption behavior preference according to a mapping association relationship between the short-term intention chain and the long-term preference chain; extracting the preference weight of each preference node in the element characteristic spectrum under different context scenes; and detecting the matching degree between the element characteristic spectrum and the current consumption behavior characteristics, and when the matching degree is lower than a preset threshold value, performing preference updating on the element characteristic spectrum based on the preference weight corresponding to each preference node and the current consumption behavior characteristics, thereby generating a commodity recommendation list. By adopting the scheme of the invention, personalized commodity recommendation based on different context scenes can be realized in an internet e-commerce platform.

The application claims a data integrity audit matching method based on psychological perception and preference, comprising the following steps: S1: calculating the subjective and objective weights of each evaluation index, and calculating the subjective and objective combination weights of the index based on the weights, and then calculating the weight of each audit scheme by using the improved Topsis comprehensive evaluation method; S2: calculating the user preference weight by using the triangular fuzzy number and Topsis method according to the user preference set, and integrating the weights of the two; S3: quantitatively calculating the comprehensive preference value of both parties according to the demand preference of the audit task and the execution preference of the audit resource, and calculating the preference utility value matrix of both parties; S4: calculating the perception utility value matrix of both parties based on the improved disappointment theory; S5: standardizing the perception utility value matrix, taking the maximization of the standard perception utility value of both parties as the optimization goal, constructing a double-objective optimization model, and solving the model. The application effectively improves the comprehensive satisfaction of both parties in the case of considering the psychological factors and preferences of both parties.

The invention discloses a user preference characterization method based on context awareness. The method comprises the following steps: defining and inputting multi-source features including an individual feature context and an instant scene context; embedding group generality and an individual characteristic context, and quantifying a multi-behavior set and an instant scene context; constructing a context-aware preference benchmark, and performing normalization processing on the context-aware preference benchmark to calculate a dynamic preference weight; weighting behavior semantic embedding representing group generality by using a dynamic preference weight to generate context-aware multi-behavior preference intensity representation; and fusing the multi-behavior preference intensity representation with the item feature representation to obtain a final user-item interaction preference intensity representation. Through a context-aware dynamic weighting mechanism, the preference intensity of the user to different behaviors can be dynamically quantified according to the individual characteristics of the user and the real-time interactive scene, so that more accurate and more adaptive user preference characterization is generated, and the performance of a recommendation system is effectively improved.

The embodiment of the invention provides a travel information processing method and device, a storage medium and a program product. In the scheme, based on semantic scene intelligent perception and preference weight dynamic allocation, the de-duplication intensity gear of each dimension can be adaptively determined, the macroscopic performance target is accurately conducted to the preference dimension hierarchy, and a hierarchical and parameterized control link is formed. And secondly, through the synergistic effect of a degradation mechanism and an upgrade mechanism, when candidate results are insufficient, intelligent circle expansion and amount compensation are performed from low to high according to preference weights, and when the results are excessive, dimension-by-dimension tightening is performed according to the preference weights, so that the number of the results is rapidly adjusted to the optimal interval while the core preference is not weakened. More importantly, according to the scheme, a traditional duplicate removal mode with a fixed threshold value is abandoned, so that a duplicate removal strategy can be continuously evolved according to a real-time scene and user preference, finally, on the premise of guaranteeing the accuracy of journey recommendation, the diversity and individuation degree of recommendation results are remarkably improved, and accurate and rich specialized journey selection is provided for the user.

Method for multi-objective optimization of a list of time slots for stakeholders (A, B, …), (a) wherein an initial list (L0) of flights in time slots is provided, (b) wherein each stakeholder (A, B, …) provides preferences in the format of a weight map (wi) comprising at least a time preference value (ti) assigned to each combination of time slot and item-ID for the respective stakeholder, (c) wherein a value of satisfaction (v) is defined that corresponds to the fulfilment of the preferences by distributing either the time slots or item-IDs in a specific list (Li), and wherein a first objective is provided that consists of generating an improved list (Lf) in which the value of satisfaction (v) is the highest, (d) wherein repeatedly for a predefined number of times two items are permutatively swapped and multiple times repeat: (e) generate a next generation of candidate lists comprising a predefined number of lists (Lc2i) by (e.1) taking a predefined number of candidate lists with best specific values of satisfaction (vi) of the given generation and for each of the chosen lists doing a predefined number of swaps according to a constraint (cm), and / or (e.2) taking a predefined number of candidate lists and combine two or more of the chosen lists according to a constraint (cc), each list having a specific value of satisfaction (vi).

The invention relates to a river-sea combined transportation path recommendation method based on user preference identification, and the method comprises the steps: S1, collecting questionnaire results of a user for cost, time and carbon emission, and respectively generating a score vector and an AHP calculation weight vector; s2, fusing the score vector and the AHP calculation weight vector to obtain a first three-target preference weight; s3, dynamically updating a second three-target preference weight in combination with the first three-target preference weight based on historical orders and behavior data of the user; s4, fusing the first three-target preference weight and the second three-target preference weight to obtain a third three-target preference weight; and S5, on the basis of the third three-target preference weight, a total target function value of each candidate combined transport path is calculated in combination with a pre-configured total target function, and the candidate combined transport paths are sorted from optimal to inferior according to the total target function values. Compared with the prior art, the method has the advantages of remarkably improving the exclusive matching degree of the recommendation scheme and the like.

This invention relates to the field of big data analytics, and discloses a method and system for analyzing consumer behavior in cultural tourism marketing. The invention quantifies the impact of context by introducing context intensity and duration, and dynamically adjusts user preference weights accordingly. This allows the system to more sensitively capture and respond to the user's real needs in the current context, avoiding erroneous recommendations caused by over-reliance on long-term preferences. Furthermore, the progressive weight recovery mechanism effectively avoids the problem of over-correction that may occur after the context ends, ensuring the consistency and stability of recommendations. Therefore, this invention effectively solves the problem in existing cultural tourism marketing systems where the recommendation decision-making mechanism becomes rigid and unable to accurately respond to the user's immediate needs when the user's travel context changes temporarily; it significantly improves the accuracy of cultural tourism marketing recommendations and user satisfaction, optimizes user experience, and improves the utilization efficiency of marketing resources.

This application belongs to the field of big data processing technology and provides a method, apparatus, device, and medium for item recommendation based on object behavior. The method acquires tracking data from multiple objects, generates a first item set based on the information in the tracking data, obtains candidate recommended items based on the similarity between items in the first item set, and then calculates a recommendation score by acquiring the preference weight coefficient, behavior weight coefficient, time decay coefficient, and second similarity of each candidate recommended item. The recommended items are then ranked according to the recommendation scores to obtain target recommended items and recommended to the target object. By incorporating preference weight coefficients, behavior weight coefficients, and time decay coefficients—which characterize the target object's preference for items—the recommendation results are optimized, improving the degree of personalized item recommendation.