10,397 research outputs found
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting
more and more attention in recent years due to their excellent performance in
accuracy. Representing user-item interactions as a bipartite graph, a GNN model
generates user and item representations by aggregating embeddings of their
neighbors. However, such an aggregation procedure often accumulates information
purely based on the graph structure, overlooking the redundancy of the
aggregated neighbors and resulting in poor diversity of the recommended list.
In this paper, we propose diversifying GNN-based recommender systems by
directly improving the embedding generation procedure. Particularly, we utilize
the following three modules: submodular neighbor selection to find a subset of
diverse neighbors to aggregate for each GNN node, layer attention to assign
attention weights for each layer, and loss reweighting to focus on the learning
of items belonging to long-tail categories. Blending the three modules into
GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified
recommendation. Experiments on real-world datasets demonstrate that the
proposed method can achieve the best diversity while keeping the accuracy
comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
A Diffusion model for POI recommendation
Next Point-of-Interest (POI) recommendation is a critical task in
location-based services that aim to provide personalized suggestions for the
user's next destination. Previous works on POI recommendation have laid focused
on modeling the user's spatial preference. However, existing works that
leverage spatial information are only based on the aggregation of users'
previous visited positions, which discourages the model from recommending POIs
in novel areas. This trait of position-based methods will harm the model's
performance in many situations. Additionally, incorporating sequential
information into the user's spatial preference remains a challenge. In this
paper, we propose Diff-POI: a Diffusion-based model that samples the user's
spatial preference for the next POI recommendation. Inspired by the wide
application of diffusion algorithm in sampling from distributions, Diff-POI
encodes the user's visiting sequence and spatial character with two
tailor-designed graph encoding modules, followed by a diffusion-based sampling
strategy to explore the user's spatial visiting trends. We leverage the
diffusion process and its reversed form to sample from the posterior
distribution and optimized the corresponding score function. We design a joint
training and inference framework to optimize and evaluate the proposed
Diff-POI. Extensive experiments on four real-world POI recommendation datasets
demonstrate the superiority of our Diff-POI over state-of-the-art baseline
methods. Further ablation and parameter studies on Diff-POI reveal the
functionality and effectiveness of the proposed diffusion-based sampling
strategy for addressing the limitations of existing methods
Business for Sustainable Society Project
This three-year project's main objective was: "How can business/industry realise a sustainable society?" The two goals for achieving the main objective were: (1) to identify promising business models for realising a sustainable society and develop relevant methods to quantify their potential while proposing measures to promote such businesses, and (2) to clarify the conditions of business/industry activities and a direction of local development that are suitable for sustainable local society. "Research on Environmentally-Sound Business Models (BM Research)" and the "Research on Environmentally-Sound Local Industry System (LIS Research)" were implemented with these two goals in mind. They were reinforced with associated research to accomplish the goals based on the results of a progress review that was made on completion of the first half of the Third Phase
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure
Recommender systems have been acknowledged as efficacious tools for managing
information overload. Nevertheless, conventional algorithms adopted in such
systems primarily emphasize precise recommendations and, consequently, overlook
other vital aspects like the coverage, diversity, and novelty of items. This
approach results in less exposure for long-tail items. In this paper, to
personalize the recommendations and allocate recommendation resources more
purposively, a method named PIM+RA is proposed. This method utilizes a
bipartite network that incorporates self-connecting edges and weights.
Furthermore, an improved Pearson correlation coefficient is employed for better
redistribution. The evaluation of PIM+RA demonstrates a significant enhancement
not only in accuracy but also in coverage, diversity, and novelty of the
recommendation. It leads to a better balance in recommendation frequency by
providing effective exposure to long-tail items, while allowing customized
parameters to adjust the recommendation list bias
A food recipe recommendation system based on nutritional factors in the Finnish food communit
Abstract. This thesis presents a comprehensive study on the relationships between user feedback, recipe content, and additional factors in the context of a recipe recommendation system. The aim was to investigate the influence of various factors on user ratings and comments related to nutritional variables, while also exploring the potential for personalized recipe suggestions. Statistical analysis, clustering techniques, and sentiment analysis were employed to analyze a dataset of food recipes and user feedback. We determined that user feedback is a complex phenomenon influenced by subjective factors beyond recipe content alone. Cluster analysis identified four distinct clusters within the dataset, highlighting variations in nutritional values and sentiment among recipes. However, due to an imbalanced distribution within the clusters, these relationships were not considered in the recommendation system. To address the absence of user-related data, a content-based filtering approach was implemented, utilizing nutritional factors and a health factor calculation. The system provides personalized recipe recommendations based on nutritional similarity and health considerations. A maximum limit of 20 recommended recipes was set, allowing users to specify the desired number of recommendations. The accompanying API also provides a mean squared error metric to assess recommendation quality. This research contributes to a better understanding of user preferences, recipe content, and the challenges in developing effective recommendation systems for food recipes
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