Nonnegative matrix tri-factorization with user similarity for clustering in point-of-interest

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell

Summary

Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes' embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes' clusters and the limitations of the functional annotations' resources hinder the identification of the currently unknown cell's functioning mechanisms. We propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species.

Availability and implementation

Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.

Using Attributes Explicitly Reflecting User Preference in a Self-Attention Network for Next POI Recommendation

With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.

Relational POI recommendation model combined with geographic information

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.

Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.