A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

Towards psychology-aware preference construction in recommender systems: Overview and research issues

User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords, categories, and item ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits, emotional states, and cognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.

Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

Value network engagement and effects of memory-related processing during encoding and retrieval of value

Decision makers rely on episodic memory to calculate choice values in everyday life, yet it is unclear how neural mechanisms of valuation differ when value-related information is encoded versus retrieved from episodic memory. The current fMRI study compared neural correlates of subjective value while value-related information was encoded versus retrieved from memory. Scanned tasks were followed by a behavioral episodic memory test for item-attribute associations. Our analyses sought to i) identify neural correlates of subjective value that were distinct and common across encoding and retrieval, and ii) determine whether neural mechanisms of subjective valuation and episodic memory interact, reflecting cooperation or competition between systems. The study yielded three primary findings. First, we found similar subjective value-related activation in the fronto-striatal reward circuit and posterior parietal cortex across valuation phases. Second, value-related activation in select fronto-parietal and salience regions was significantly greater at value retrieval. Third, we found no evidence of an interaction between neural correlates of subjective valuation and episodic memory. Taken with prior research, our findings suggest that context-specific effects are likely to determine whether neural correlates of subjective value interact with episodic memory, and indicate that fronto-parietal and salience regions play a key role in retrieval-dependent valuation.