Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

Abstract

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users' tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences.

Application of Collaborative Filtering Algorithm in Mathematical Expressions of User Personalized Information Recommendation

“…The higher the calculated value, the higher the user similarity, and the closer the user's preference. Equation (12) shows the fuzzy score calculation method for multifeature factors in closeness calculation: In Equation (12), the degree of closeness between two users is denoted by D ( Ũ a , Ũ b ) . The inner product and outer integral of the fuzzy set determined by the multi-characteristic factor of user a and user b are respectively Ũ a ⋅ Ũ b and Ũ a ⊗ Ũ b .…”

“…Toledo et al [11] summarized the use of fuzzy tools in recommendation systems to detect more common research topics and research gaps, so as to propose future research directions for promoting the current development of fuzzy-based recommendation systems. Liu et al [12] set out to reduce the user's search cost in the overloaded network environment, and used the Pearson correlation coefficient to evaluate the interest of users for commodity without record to form a sparse matrix for interest degree evaluation, and a neighbor set with high similarity to the target user was analyzed. The research focused on user interest similarity as the eigenvalue research, which greatly improved the accuracy value of the previous recommendation algorithm and improves the recommendation quality.…”

Eating healthier: Exploring nutrition information for healthier recipe recommendation

“…Recently, recommendation has been a hot research problem with the booming of social networks. Scholars have carried out many research works on different fields [10,11,12,13,14], e.g., location recommendation, movie recommendation. In this paper, we focus on recipe recommendation and discuss the recent progress of the related methods and applications.…”

Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation

“…The recommendation methods based on collaborative filtering [10], [20], [26]- [28] are mainly divided into user-based collaborative filtering and location-based collaborative filtering. The POI recommendation system uses the collaborative filtering method mainly into the following three steps: generating recommendation candidate sets, calculating similarity and calculating recommendation scores of POIs.…”